oracle financial services enterprise modeling user …...oracle financial services enterprise...

Oracle Financial Services Enterprise Modeling User Guide

Release 8.0.9.0.0 Dec 2019


Release 8.0.9.0.0Dec 2019

Part Number: E69408-02

Oracle CorporationWorld Headquarters500 Oracle ParkwayRedwood Shores, CA 94065U.S.A.

Document Number: E69408-02First Edition (Dec 2019)

Copyright © 1996-2019, Oracle and/or its affiliates. All rights reserved.Printed in U.S.A. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise without the prior written permission.

TrademarksOracle is a registered trademark of Oracle Corporation and/or its affiliates.Other names may be trademarks of their respective owners.

Oracle CorporationWorld Headquarters500 Oracle ParkwayRedwood Shores, CA 94065U.S.A.Internet: www.oracle.com/financialservices

www.oracle.com/industries/financial_services

www.oracle.com/industries/financial_services

Contents

Preface ............................................................................................................................ 13

Audience .............................................................................................................................................................................. 14How this Guide is Organized ........................................................................................................................................... 14Related Documents ............................................................................................................................................................ 15Conventions Used .............................................................................................................................................................. 15

CHAPTER 1 About Oracle Financial Services Enterprise Modeling .............. 17Overview.............................................................................................................................................................................. 17

Key Features and Advantages ................................................................................................................................... 18Concepts of Oracle Financial Services Enterprise Modeling ...................................................................................... 19

Oracle Financial Services Enterprise Modeling Workflow................................................................................... 20Components of Oracle Financial Services Enterprise Modeling ................................................................................ 21

Sandbox ........................................................................................................................................................................ 21Sandbox Maintenance................................................................................................................................................. 21Techniques Registration............................................................................................................................................. 22Variable Definition...................................................................................................................................................... 22Modeling....................................................................................................................................................................... 22

User Groups and Entitlements......................................................................................................................................... 22

CHAPTER 2 General Features ........................................................................... 23Home Page Components .................................................................................................................................................. 23

Header........................................................................................................................................................................... 24Sandbox option is available only if EMF is enabled in your OFSAA application. ........................................... 25Navigation List ............................................................................................................................................................ 25Tab Bar ......................................................................................................................................................................... 25

Acronyms............................................................................................................................................................................. 26

CHAPTER 3 Getting Started............................................................................... 27Logging into the Oracle Financial Services Enterprise Modeling Application......................................................... 27Changing Password ............................................................................................................................................................ 28

CHAPTER 4 Managing Sandbox........................................................................ 31About Sandbox ................................................................................................................................................................... 31Workflow ............................................................................................................................................................................. 31

Physical Sandbox Workflow...................................................................................................................................... 32User Roles............................................................................................................................................................................ 32Managing Sandbox ............................................................................................................................................................. 33

Oracle Financial Services Enterprise Modeling User Guide 5

Contents

Accessing Sandbox Definition .................................................................................................................................. 33Creating Sandbox ........................................................................................................................................................ 34Viewing Sandbox Definition ..................................................................................................................................... 37Modifying Sandbox Definition ................................................................................................................................. 38Searching Sandbox Definition .................................................................................................................................. 38Sandbox Maintenance................................................................................................................................................. 38

CHAPTER 5 Managing Techniques ................................................................... 43About Techniques .............................................................................................................................................................. 43

Technique Registration Workflow ........................................................................................................................... 44User Roles and Functions.......................................................................................................................................... 44

Managing Techniques ........................................................................................................................................................ 45Accessing Techniques................................................................................................................................................. 45Creating a Technique Based on Scripts ................................................................................................................... 45Authorizing a Technique............................................................................................................................................ 49Viewing a Technique .................................................................................................................................................. 49Modifying a Technique............................................................................................................................................... 50Copying a Technique.................................................................................................................................................. 50Deleting a Technique.................................................................................................................................................. 50Searching Techniques ................................................................................................................................................. 51

CHAPTER 6 Managing Variables ....................................................................... 53About Variable Definition................................................................................................................................................. 54Managing Variables ............................................................................................................................................................ 54

Accessing Variable Management .............................................................................................................................. 55Adding Variable Definition ....................................................................................................................................... 56Viewing Variable Definition...................................................................................................................................... 58Searching Variables ..................................................................................................................................................... 59Modifying Variable Definition .................................................................................................................................. 59Copying Variable Definition ..................................................................................................................................... 59Deleting Variable Definition ..................................................................................................................................... 60

CHAPTER 7 Managing Modeling ....................................................................... 61About Modeling.................................................................................................................................................................. 61

Modeling Workflow.................................................................................................................................................... 62User Roles and Functions.......................................................................................................................................... 63

Managing Modeling............................................................................................................................................................ 63Accessing Model Management.................................................................................................................................. 63Model Versioning........................................................................................................................................................ 64Modeling using Open R with HDFS Option ......................................................................................................... 64Creating and Executing a Model............................................................................................................................... 67Model Parameters ....................................................................................................................................................... 75Viewing Model Definition ......................................................................................................................................... 79

6 Oracle Financial Services Enterprise Modeling User Guide

Contents

Modifying Model Definition ..................................................................................................................................... 80Deleting Model Definition......................................................................................................................................... 80Searching Model Management .................................................................................................................................. 80

Model Chaining................................................................................................................................................................... 81Model Execution Status..................................................................................................................................................... 81

Accessing Model Execution Status........................................................................................................................... 81Searching Model Execution Status ........................................................................................................................... 82

Model Outputs .................................................................................................................................................................... 82Accessing Model Outputs.......................................................................................................................................... 82Searching Model Outputs .......................................................................................................................................... 83View and Compare Model Outputs ......................................................................................................................... 83

Model Deployment ............................................................................................................................................................ 86Accessing Model Deployment .................................................................................................................................. 86Searching Model Deployment................................................................................................................................... 86Requesting and Authorizing Model Deployment .................................................................................................. 87

Model Execution ................................................................................................................................................................ 88Request for Model Execution ................................................................................................................................... 88Champion Challenger ................................................................................................................................................. 90Requesting New Champion....................................................................................................................................... 91

Configure Execution Venue ............................................................................................................................................. 92Prerequisites ................................................................................................................................................................. 92Accessing Configure Execution Venue ................................................................................................................... 92Adding Model Execution Configuration................................................................................................................. 92Viewing Model Execution Configuration ............................................................................................................... 94Modifying Model Execution Configuration............................................................................................................ 94Deleting Model Execution Configuration............................................................................................................... 94

CHAPTER 8 Managing Stress Testing .............................................................. 97About Stress Testing .......................................................................................................................................................... 97

........................................................................................................................................................................................ 98Stress Definition ................................................................................................................................................................. 98

Accessing Stress Definition ....................................................................................................................................... 99Adding Stress Definition............................................................................................................................................ 99Executing Stress Definition..................................................................................................................................... 101Viewing Stress Definition ........................................................................................................................................ 102Modifying Stress Definition .................................................................................................................................... 103Deleting Stress Definition........................................................................................................................................ 103Searching Stress Definition...................................................................................................................................... 103

CHAPTER 9 Creating and publishing models using integration of OFS EM with EMF Studio........................................................................... 105

Prerequisites....................................................................................................................................................................... 105Logging into EMF Studio ............................................................................................................................................... 106


Contents

Creating Notebooks ......................................................................................................................................................... 107Editing a Notebook .................................................................................................................................................. 108

Creating Paragraphs.......................................................................................................................................................... 109Publishing Models ............................................................................................................................................................ 113Using python to create notebooks and paragraphs, and publishing models........................................................... 114

APPENDIX A References .................................................................................. 119R Scripts and Oracle R Enterprise (ORE) Statistical Functions ............................................................................... 120

Data usage when R script is used entirely ............................................................................................................. 120Data usage when ORE native implementation is employed - Oracle Financial Services Enterprise Modeling application - Specifics to be considered................................................................................................................. 120Data Handling............................................................................................................................................................ 121Logging ....................................................................................................................................................................... 122

Fitting and Prediction Using R Script ........................................................................................................................... 123R scripting .................................................................................................................................................................. 123ORE scripting............................................................................................................................................................ 123

APPENDIX B User Groups ................................................................................ 125User Groups ...................................................................................................................................................................... 125User Group - Role Mapping ........................................................................................................................................... 126

APPENDIX C .................................................................EMF Object Migration 131Prerequisites ............................................................................................................................................................... 131

............................................................................................................................................................................................. 132


List of Figures

Figure 1. Oracle Financial Services Enterprise Modeling Workflow................................................................................. 20Figure 2. Home Page ................................................................................................................................................................. 23Figure 3. Oracle Financial Services Enterprise Modeling Application Login Page ......................................................... 27Figure 4. Home Page ................................................................................................................................................................. 28Figure 5. Change Password Window ...................................................................................................................................... 29Figure 6. Physical Sandbox Workflow.................................................................................................................................... 32Figure 7. Sandbox Definition Page ......................................................................................................................................... 33Figure 8. Schema-based Sandbox Definition Window ........................................................................................................ 35Figure 9. Sandbox Maintenance Page ..................................................................................................................................... 39Figure 10. Technique Registration Workflow........................................................................................................................ 44Figure 11. Technique Registration........................................................................................................................................... 45Figure 12. Technique Definition.............................................................................................................................................. 46Figure 13. Script Console.......................................................................................................................................................... 47Figure 14. Inputs window......................................................................................................................................................... 48Figure 15. Outputs window...................................................................................................................................................... 48Figure 16. Variable Management ............................................................................................................................................. 55Figure 17. Variable Definition.................................................................................................................................................. 56Figure 18. Model Creation Workflow..................................................................................................................................... 62Figure 19. Model Management ................................................................................................................................................ 64Figure 20. Model Definition page............................................................................................................................................ 68Figure 21. Model Definition..................................................................................................................................................... 72Figure 22. Scripting Console .................................................................................................................................................... 73Figure 23. Outputs window...................................................................................................................................................... 77Figure 24. Model Script Console ............................................................................................................................................. 81Figure 25. Model Execution Status ......................................................................................................................................... 82Figure 26. Model Outputs ........................................................................................................................................................ 83Figure 27. Outputs Summary ................................................................................................................................................... 83Figure 28. Output window for R scripted models ................................................................................................................ 84Figure 29. Model Deployment Page........................................................................................................................................ 86Figure 30. Request for Model Execution ............................................................................................................................... 89Figure 31. Champion Challenger ............................................................................................................................................. 90Figure 32. Model Execution Configuration ........................................................................................................................... 92Figure 33. Stress Testing Workflow ........................................................................................................................................ 98Figure 34. Stress Definition Page ............................................................................................................................................ 99Figure 35. Stress Definition Page .......................................................................................................................................... 100Figure 36. EMF Studio Login Window ................................................................................................................................ 106Figure 37. EMF Studio Home Window ............................................................................................................................... 107Figure 38. Create Notebook Popup ...................................................................................................................................... 107Figure 39. Notebook Details Popup ..................................................................................................................................... 108


List of Figures

Figure 40. Create Paragraph Window ................................................................................................................................... 109Figure 41. Paragraph Window................................................................................................................................................ 109Figure 42. Dataset query result example in EMF Studio ................................................................................................... 110Figure 43. Hover mouse pointer to view Add Paragraph button..................................................................................... 110Figure 44. Variables query result example in EMF Studio ................................................................................................ 111Figure 45. Dataframes query result example in EMF Studio ............................................................................................ 111Figure 46. Example of RScript formula application on dataframes result in EMF Studio .......................................... 112Figure 47. Publish Notebook Popup .................................................................................................................................... 113Figure 48. Republish Notebook Popup................................................................................................................................ 113Figure 49. List of workspaces for python result example.................................................................................................. 114Figure 50. Attach workspace python query result in EMF Studio ................................................................................... 115Figure 51. Dataset python query result example in EMF Studio ..................................................................................... 115Figure 52. Variables python query result example in EMF Studio................................................................................... 116Figure 53. Dataframes python query result example in EMF Studio .............................................................................. 116Figure 54. Example of python formula application on dataframes result in EMF Studio ........................................... 117Figure 55. Example of python instruction to publish to OFSAA.................................................................................... 117


List of Tables

Table 1. Conventions Used in this Guide ............................................................................................................................. 15Table 2. Features ....................................................................................................................................................................... 18Table 3. Schema based Sandbox - Field Descriptions ........................................................................................................ 35Table 4. R script based Technique Definition - Field Description ................................................................................... 46Table 5. Variable Definition - Field Descriptions................................................................................................................ 56Table 6. Model Definition - Field Descriptions ................................................................................................................... 69Table 7. Execution Venue Details.......................................................................................................................................... 93Table 8. Stress Definition - Field Descriptions .................................................................................................................. 100Table 9. User Groups ............................................................................................................................................................. 125Table 10. User Group to Role Mapping.............................................................................................................................. 126


List of Tables


Preface

The Oracle Financial Services Enterprise Modeling User Guide explains the concepts of Oracle Financial Services Enterprise Modeling (OFS EM) and provides step-by-step instructions for navigating through the application.

This chapter discusses the following topics:

Audience

How this Guide is Organized

Related Documents

Conventions Used in This Guide


Preface

Audience

The Oracle Financial Services Enterprise Modeling User Guide is designed for various users of Oracle Financial Services Enterprise Modeling. This guide is intended for the following users:

Business Analysts (BA) who are instrumental in solution designing and creating statistical models using historical data.

System Administrators (SA) who are instrumental in maintaining and executing batches, making the application secure and operational, and configuring the users and security of the application.

How this Guide is Organized

The Oracle Financial Services Enterprise Modeling User Guide includes the following topics:

Chapter 1: About Oracle Financial Services Enterprise Modeling provides an overview of the Oracle Financial Services Enterprise Modeling, its features, the workflow, user roles, privileges, and actions.

Chapter 2: General Features, explains the general features of Oracle Financial Services Enterprise Modeling. These are the common User Interface (UI) features that are found across all modules.

Chapter 3: Getting Started explains how to access and login to the application.

Chapter 4: Managing Sandbox gives detailed information on Sandbox module, user roles, creating Sandbox definitions, maintaining the Sandbox, and so on.

Chapter 5: Managing Techniques describes techniques registration, creating, authorizing, editing, and deleting a technique.

Chapter 6: Managing Variables explains about creating, editing, viewing, and deleting variables.

Chapter 7: Managing Modeling gives detailed information about creating, editing, viewing, and deleting models. It also explains how to execute and deploy models.

Chapter 8: Managing Stress Testing describes about creating, editing, viewing, and deleting stress definitions.

Chapter 9: Creating and publishing models using the integration of Oracle Financial Services Enterprise Modeling and EMF Studio.


Preface

Related Documents

This section identifies additional documents related to OFS AAAI Application Pack. You can access Oracle documentation online from the Oracle Help Center (OHC )Documentation Library.

Oracle Financial Services Advanced Analytical Applications Infrastructure (OFS AAAI) Application Pack 8.0.9.0.0 Installation and Configuration Guide

Oracle Financial Services Analytical Applications Infrastructure Environment Check Utility Guide

Oracle Financial Services Analytical Applications Infrastructure User Guide

Oracle Financial Services Analytical Applications Infrastructure Administration Guide

To find additional information about how Oracle Financial Services solves real business problems, see our Web site at www.oracle.com/financialservices.

Conventions Used

Table 1 lists the conventions used in this guide.Table 1. Conventions Used in this Guide

Convention MeaningItalics Names of books, chapters, and sections as references

EmphasisBold Object of an action (menu names, field names, options,

button names) in a step-by-step procedure Commands typed at a prompt User input

Monospace Directories and subdirectories File names and extensions Process names Code sample, including keywords and variables within text

and as separate paragraphs, and user-defined program elements within text

<Variable> Substitute input value


http://docs.oracle.com/cd/E60058_01/homepage.htm

Preface


CHAPTER 1 About Oracle Financial Services Enterprise Modeling

This chapter provides an insight to Oracle Financial Services Enterprise Modeling. This chapter includes the following topics:

Overview

Concepts of Oracle Financial Services Enterprise Modeling

Components of Oracle Financial Services Enterprise Modeling

User Groups and Entitlements

Overview

The core of Financial Institutions Models is Risk, Marketing, Financial Crime and Enterprise Performance Analytical Applications. These models include traditional statistical techniques, modern machine learning methods, computational and simulation models. Oracle Financial Services Enterprise Modeling leverages popular statistical platforms such as the R platform and presents a framework for developing, deploying and managing models at the enterprise level, for financial institutions.As an enterprise modeling toolkit, Oracle Financial Services Enterprise Modeling enables an institution's IT policies to be enforced while providing flexibility and freedom that Data Scientists and Statistical Modelers desire. Administrative users grant analysts and modelers access to sandboxes - particular analytical subject areas of interest along with a subset of production data - for model building. Validated and approved models may then be promoted from sandboxes to the enterprise model repository. Models in the repository may then be woven into in analytical application flows crafted by mixing data management tasks, model execution and deterministic business logic.As the use of models proliferate and as modeling becomes a self-service idea within financial institutions, authorized modelers may publish techniques -- parameterized templates of models that serve as building blocks or standardized blueprints for models - so that the best ideas from experienced modelers are captured and reused within the firm. Oracle Financial Services Enterprise Modeling supports techniques developed using R language.Unique to the needs of large and medium sized financial institutions is the need to project capital levels under a variety of macroeconomic conditions, in order to assess the institution's financial strength under different stress scenarios. The Stress Testing Framework within Oracle Financial Services Enterprise Modeling enables risk and finance officers to define various shocks and scenarios and to apply these conditions uniformly across different model execution runs.Data lineage and traceability are central to a Financial Intuition's governance process. Oracle Financial Services Enterprise Modeling Application together with the pre-requisite Oracle Financial Services Analytical Applications Infrastructure Application provides a toolkit for developing complete end-to-end analytical applications with data lineage and traceability enabled at every step along the analytical workflow.


OverviewChapter 1–About Oracle Financial Services Enterprise Modeling

Key Features and Advantages

The key features and advantages of Oracle Financial Services Enterprise Modeling are:Table 2. Features

Model Registry Centrally Manage and Control all in the enterpriseStatistical Technique Library Catalog and publish best modeling practices via reusable templates, which are to

be used as building blocks for modelsTrack model usage across applications

Real-time view of dependencies such as which applications use which models; which variables are used a model, etc. Perform what-if impact analysis for changes to data sets, variables and models.

Modeling Sandboxes Provision sandboxes with subsets of production data for modelers giving modelers complete freedom to build, test and calibrate models using production data, while keeping production environment locked-down for security and compliance needs. Readily deploy built models to locked down production via a built-in workflow.

Integrated Platform (With Oracle Financial Services Analytical Applications Infrastructure)

Statistical modeling, Data management and application deployment integrated in a unified platform so that models may be deployed for use within analytical applications quickly

Execute Models local to data In-database (Oracle) execution of statistical models for high performance and scalability.

Foundation for Enterprise Stress testing

Enable baseline and stress executions of models defined within the platform

Enterprise-R support with Oracle Financial Services Enterprise ModelingOracle Financial Services Enterprise modeling application leverages the R statistical platform. R is an open source statistical programming language and environment for computing and graphics. For more information about R, see the R Project for Statistical Computing at http://www.r-project.org. Oracle R Enterprise (ORE) is an in-database implementation of the R platform. ORE is a component of the Oracle Advanced Analytics Option of Oracle Database Enterprise Edition. ORE enables running R models within the Oracle Database Environment. For more information on ORE, see the Oracle R Enterprise User Guide.Oracle Financial Services Enterprise Modeling Application supports ORE based models and techniques, that is, models defined and registered within the platform may be executed on an ORE instance. R models may be scripted within the platform or may be imported into the platform from previously existing R scripts.


Concepts of Oracle Financial Services Enterprise ModelingChapter 1–About Oracle Financial Services Enterprise Modeling

Concepts of Oracle Financial Services Enterprise Modeling

Oracle Financial Services Enterprise Modeling is built specifically to meet many of the needs of large Financial Institutions where external regulatory and internal governance policies.Models may only be built and tested in a sandbox environment. A sandbox has to be provisioned and authorized for use (usually by an administrator) before it is visible to modelers. Any number of sandboxes may be provisioned, but generally, an enterprise may provision a sandbox for each department or analytical team. For e.g, there may be an LGD sandbox consisting of data needed to build and validate LGD models, and a separate one for PD modeling. Such segregation of modeling teamwork areas is desirable in practice, but it is not a requirement: here may be as few as a single sandbox for the entire organizationSandboxes are provisioned along with data required for modeling. Tools are provided to aid administrators in provisioning sandboxes with subsets of production data. Datasets and variables abstract physical data sources from the modeler, and data in the sandbox is exposed to modelers using via datasets and variables. Models are built against datasets and variables not physical data tables and columns. i.e, the underlying data is exposed as a logical dataset within the application, and modelers need not write any database specific queries to obtain data for modeling. It is generally an administrative task to define datasets, and have a menu of datasets available for the modelers. When a sandbox is provisioned, one or more datasets can be associated with the sandbox.Models in a sandbox can be changed (created/ edited) by anyone with access to the sandbox. Model versions are preserved in the sandbox along with execution and output histories. Once a model has been validated in the sandbox environment, the modeler may request that model to be promoted to the locked-down "production" environment, and once promoted, the promoted model cannot be altered.Modelers may create new models by using a registered technique from the technique library, as a template. A technique is simply a parameterized and reusable script. An enterprise may publish a menu of techniques and require that modelers use those techniques as the foundation for models. The act of model building is then reduced to selecting an appropriate technique, binding the technique to the appropriate dataset and variables and providing runtime parameters to the script. Generally, a central data sciences team within an enterprise or a department is responsible for publishing techniques.Not all models can be built using published techniques, and so an alternate way to build a model is to write an R script 'from scratch' and execute the script as a model. Regardless of how the model is built, the model must be bound to dataset and variables before it can access data. A business analyst may decide to include a published model (that is a promoted model) in an application flow. For e.g., a capital computation application flow may include many steps, some of which may be steps to execute statistical models for computing PD and LGD. As the Oracle Financial Services Enterprise Modeling application is fully integrated with the Oracle Financial Services Analytical Applications Infrastructure (AAI), models promoted from Oracle Financial Services Enterprise Modeling application are available as tasks in AAI, and so can be included in any orchestrated execution of tasks (application run). Note that within a sandbox, executable models are also made available as tasks private to the sandbox, and so can be included in sandbox specific orchestration of tasks.


Concepts of Oracle Financial Services Enterprise ModelingChapter 1–About Oracle Financial Services Enterprise Modeling

Oracle Financial Services Enterprise Modeling Workflow

Figure 1. Oracle Financial Services Enterprise Modeling Workflow


Components of Oracle Financial Services Enterprise ModelingChapter 1–About Oracle Financial Services Enterprise Modeling

Components of Oracle Financial Services Enterprise Modeling

This section describes the components of Oracle Financial Services Enterprise Modeling.The following are the components of Oracle Financial Services Enterprise Modeling application:

Sandbox

Sandbox Maintenance

Techniques Registration

Variable Definition

Modeling

You can create models in a Schema Based sandbox.

Sandbox

Sandbox in Oracle Financial Services Enterprise Modeling refers to a restricted modeling environment, where the Data model is uploaded. It is implemented as an information domain. The following information domains are required to perform operations in Oracle Financial Services Enterprise Modeling.

Sandbox Information Domain: This information domain is used to create and execute business models. Based on the execution status and the generated output, you can verify the results and deploy the model into production.

You can create the following type of Sandbox in the Oracle Financial Services Enterprise Modeling application.

Physical Sandbox: A Physical Sandbox is a restricted environment outside the Production information domain where there is actual movement of data from the production information domain to the physical sandbox. Creating a sandbox with multiple datasets eliminates the need for having a sandbox definition for each dataset. You can upload the data model while defining the sandbox, or upload it later using the Import Model option in Data Model Maintenance. When you save the sandbox definition, the required tables are created in the sandbox information domain. However, data present in the tables are copied only after authorizing sandbox population from the Sandbox Maintenance.

Production Information Domain: This information domain allows you to request for model execution, and generate model outputs.

Note: Ensure the data model of the sandbox information domain should be a sub-set of the data model of the production information domain.

Sandbox Maintenance

Sandbox Maintenance helps you to populate the data to the tables in the Sandbox information domain, based on the dataset and the filters in the Sandbox definition. You have the option to do complete or incremental sandbox data population. The Oracle Financial Services Enterprise Modeling application enables you to synchronize the different versions of a Data Model which exists in a Production and Sandbox Information Domain through Incremental Data Model


User Groups and EntitlementsChapter 1–About Oracle Financial Services Enterprise Modeling

Upload. You can refresh the details and fetch the incremental data model changes from Production to Sandbox Information Domain.

Techniques Registration

Technique is a set of generalized statistical algorithms which can be used to build analytical models. Oracle Financial Services Enterprise Modeling application is based on techniques developed using R script and ORE functions.

Variable Definition

Variable refers to a logical set of attributes that are likely to change based on the selected parameters. In a modeling environment variable plays a vital role in filtering the model parameters and to derive an estimate based on historical data. Variables are defined in production information domain.

Modeling

Modeling refers to the process of designing a prototype based on a structured data model, considering all the variables for statistical analysis and to simulate real events and processes.You can use the Modeling utility to measure and quantify risk. You can use the pre-defined models to predict business trends and validate the existing models. You can use R scripting (using R functions as well as ORE functions) or Open R to create business models. See Enterprise-R support with Oracle Financial Services Enterprise Modeling section for more information.Oracle Financial Services Enterprise Modeling enables you to run and execute R functions as well as ORE functions in the database, thereby greatly increasing scalability and performance.

You can create models in a Schema Based sandbox.

User Groups and Entitlements

The following table gives the details about the User Groups in the Oracle Financial Services Enterprise Modeling Application.Table 1. User Groups

User Group Name User Group DescriptionModeling Administrator User mapped to this group will have access to all the menu items for Enterprise

modeling and will be have authorization rights for sandbox population, model deployment and modeling technique authorization.

Modeler Group User mapped to this group will have access to all the menu items for Enterprise modeling but will not have authorization rights for sandbox population, model deployment and modeling technique authorization.


CHAPTER 2 General Features

This chapter explains the general features of Oracle Financial Services Enterprise Modeling application UI. These are the common features that are found across the modules of Oracle Financial Services Enterprise Modeling. It describes the organization of the user interface and provides step-by-step instructions for navigating through the application.

This chapter includes the following topics:

Home Page Components

Acronyms

Home Page Components

The Home Page contains the following sections.

Header

Navigation List

Tab Bar

Content

Footer

Figure 2. Home Page


Home Page ComponentsChapter 2–General Features

Header

The Header displays icons, buttons and text for generic information and access to the OFSAA application’s features. The following user-interface elements are displayed for EMF:

Applications: Click this icon to display applications in a Tiles menu on the content window.

Administration: Click this icon to display administration tools in a Tiles menu on the content window.

Sandbox: Click this icon to display Sandboxes in a Tiles menu on the content window.

Language: Displays the selected language. Click to select from the options in the drop down.

User Name: Displays the logged in user name. Click to select from the following options in the drop down:

Preferences: Select to set the Home page.

About: Select to view the copyrights and third-party information.

Change Password: Select to change the password in the Change Password window.

Logout: Select to log out of the application.

Last login date and time, and last failed login date and time: Click to view the last login date and time, and the last failed login date and time.

Discover/Explore OFSAA Apps: Click to view the OFSAA applications web page for more details.

ApplicationsThe applications available are displayed in a Tiles menu on the content window. Click the Tiles to open the selected application. For EMF, the following applications are available:

Financial Services Enterprise Modeling

Data Management For more information, see the Oracle Financial Services Analytical Applications Infrastructure User Guide.

Sandbox Maintenance

Model Execution

Technique Registration

Stress Testing

Configure Execution Venue

Common Tasks For more information, see the Oracle Financial Services Analytical Applications Infrastructure User Guide.

Data Model Management

Unified Analytical Metadata

Operations

Data Entry Forms and Queries

Utilities

Object Administration


Home Page ComponentsChapter 2–General Features

Metadata Browser

AdministrationThe administration feature displays on Tiles menu. Click the Tiles navigate further. For more information, see the Oracle Financial Services Analytical Applications Infrastructure User Guide. For EMF, the following applications are available:

Translation Tools

Processing Modeling Framework (link to PMF User Guide)

SandboxThe Sandbox icon from the header displays options in Tiles menu. Click on the menu to display the following menu items:

Modeling

Model Creation

Model Execution Status

Model Output

Model Deployment

Sandbox option is available only if EMF is enabled in your OFSAA application.

Navigation List

The Navigation list is a slide-in slide-out UI element that appears on clicking the Menu (three lines) icon. This element displays a list of links in a menu based on the application selected from the Applications menu and the access rights assigned to the logged in user. Click on the various links in the list to navigate further and open in the content window. For more details, seethe Oracle Financial Services Analytical Application Infrastructure User Guide.

Tab Bar

The Tab bar contains icons and text to navigate to specific OFSAA application modules. Click on the Home button in Tabs to navigate to the Applications page on the content window. The elements displayed in Tabs is dependent on the OFSAA application logged into and the access roles assigned to the user.



AcronymsChapter 2–General Features

Acronyms

The following table describes the acronyms commonly used in this application.Table 2. Acronyms

Acronyms DescriptionBA Business AnalystsInfodom Information DomainLHS menu Left hand side menuOFSAAI Oracle Financial Services Analytical Application InfrastructureOFSAA Oracle Financial Services Analytical ApplicationsProduction Infodom Production Information DomainRHS menu Right hand side menuSandbox Infodom Sandbox Information DomainSA System AdministratorURL Uniform Resource LocatorUI User Interface


CHAPTER 3 Getting Started

This chapter explains different steps to login to the Oracle Financial Services Enterprise Modeling application.

The following sections are included in this chapter:

Logging into the Oracle Financial Services Enterprise Modeling Application

Changing Password

Logging into the Oracle Financial Services Enterprise Modeling Application

Once the application is installed and configured, you can access Oracle Financial Services Enterprise Modeling Application.

To access Oracle Financial Services Enterprise Modeling, follow these steps:

1. Enter the OFSAAI URL in your browser. The Login window is displayed.

Figure 3. Oracle Financial Services Enterprise Modeling Application Login Page

2. Select the Language.

3. Enter your User ID and Password.

4. Click Login.


Changing PasswordChapter 3–Getting Started

5. Click from the header to display the applications in a Tiles menu.

6. Select Financial Services Enterprise Modeling from the Tiles menu. The Enterprise Modeling Application Home Page is displayed with the Navigation list to the left.

Figure 4. Home Page

Changing Password

The first time you log in to the application, the Change Password window is displayed. Alternatively, you can also choose to change the password at any time.To change password, follow these steps:



1. Click the User Name drop down and select Change Password from the header at the top to display the Change Password window.

Figure 5. Change Password Window

2. Enter the User ID.

3. Enter the Old Password.

4. Enter New Password and re-enter in the Confirm Password field.

5. Click OK.

Note:

Passwords are displayed as asterisks while you enter. This is to ensure that the password is not revealed to other users.

Ensure that the entered password is at least six characters long.

The password must be alphanumeric with a combination of numbers and characters.

The password should not contain spaces.

Passwords are case sensitive and ensure that the Caps Lock is not turned ON.

By default, the currently used password is checked for validity if password history is not set.

The new password should be different from previously used passwords based on the password history, which can be configured. If you encounter any of the following problems, contact the System Administrator:

Your user ID and password are not recognized.

Your user ID is locked after three consecutive unsuccessful attempts.

Your user ID has been disabled.

Guest user cannot change the password.


CHAPTER 4 Managing Sandbox

This chapter provides information about Sandbox in the Oracle Financial Services Enterprise Modeling application and step-by-step instructions to use this section.


About Sandbox

Workflow

User Roles

Managing Sandbox

About Sandbox

Sandbox is a restricted environment in which you can analyze the data on trial and error basis and come up with actual analysis that helps predict the risks and business opportunities for banking institutions. You can create a sandbox by selecting the required datasets and the information domain which you want to implement as the sandbox.

Workflow

This section explains the workflow of Sandbox. There is one type of Sandbox: Physical Sandbox


User RolesChapter 4–Managing Sandbox

Physical Sandbox Workflow

The following image gives a detailed workflow of Physical Sandbox.

Figure 6. Physical Sandbox Workflow

User Roles

This section explains about the user roles required to define a Sandbox. To create a sandbox, you must have the following use roles:

SANDBXCR

SANDBXAUTHAn authorizer must authorize the sandbox population and deletion. To populate and delete a Sandbox, you must have the following user roles:

SANDBXMOD

SANDBXAUTH

Note: For more information on User Groups, see Appendix A, “References.”


Managing SandboxChapter 4–Managing Sandbox

Managing Sandbox

This section explains how to manage Sandbox feature in Oracle Financial Services Enterprise Modeling application.This section includes the following topics:

Accessing Sandbox Definition

Creating Sandbox

Viewing Sandbox Definition

Modifying Sandbox Definition

Searching Sandbox Definition

Sandbox Maintenance

Accessing Sandbox Definition

You can access the Sandbox Definition page from the Oracle Financial Services Enterprise Modeling Home page.To access the Sandbox Definition page, follow these steps:

1. Login to Oracle Financial Services Enterprise Modeling.



4. Click Financial Services Enterprise Modeling from the Navigation list to expand view the menu.

5. Click Sandbox Maintenance from the expanded Navigation list to view the next level of links in the menu. Click Sandbox Creation to display the Sandbox Definition window.

Figure 7. Sandbox Definition Page



Creating Sandbox

This feature allows you to create a sandbox where you can create and execute models.

Note:

1. After creating a Sandbox you should refresh the screen, so that the newly created sandbox is displayed in the Sandbox tab.

2. If your OFSAA application is configured for Data Redaction (Advanced Security options), then your system administrator has to give certain Grants related to Data Redaction to the Sandbox schema for model execution to execute. See the section Data Redaction Grants to Sandbox Schema in the OFSAAI 8.0.9.0.0 Administrator Guide.

This section includes the following topics:

Creating Schema-based Sandbox Definition

Creating Schema-based Sandbox DefinitionA Schema-based Sandbox is a restricted environment outside the Production information domain, where there is actual movement of data from the production information domain to the physical sandbox. A schema-based sandbox is also called a physical sandbox. A sandbox can be created with multiple datasets, which eliminates the need for having a sandbox definition for each dataset. To create a schema-based sandbox, follow these steps:

1. Select from the Sandbox Definition tool bar. The Sandbox Definition window is displayed.

Note: The Add button is disabled if you select a checkbox in the grid.


https://docs.oracle.com/cd/E60058_01/PDF/8.0.6.x/8.0.6.0.0/OFSAAI_Administration_Guide_8.0.6.0.0.pdf





Figure 8. Schema-based Sandbox Definition Window

2. Enter the following details:Table 3. Schema based Sandbox - Field Descriptions

Field DescriptionFields marked in red asterisk (*) are mandatory.Sandbox Name Enter a name for the sandbox.

Note: Ensure that there are no special characters like `, {,},", ', ~, <,>, /, \, and multiple spaces.

Sandbox Description Enter a brief description for the sandbox.



Source Dataset Select the required datasets. The datasets you want to select should match the Data Model of the sandbox.

1. Click and open the Source Dataset Browser. All the Authorized Datasets in the selected production Infodom are listed in the Hierarchical Browser.

2. Select the required dataset from the Datasets pane and click .

To deselect a dataset select it from the Selected Datasets pane and click .

3. Click to view the details of the selected datasets.

4. Click to create a dataset. For more details on creating a dataset, see Creating Data Set section in Oracle Financial Services Analytical Applications Infrastructure User Guide.

Note: The Datasets based on Derived Entities are not supported.Is data model upload required? By default, the Yes option is selected.

Select Yes to upload the data model while saving the sandbox definition. Select No if the data model is already uploaded.

Sandbox Location This is the information domain you want to implement as sandbox.

1. Click and open the Sandbox Location Browser. If Yes is selected for Is data model upload required? option, the Sandbox

Location pane displays the list of the Information Domains where data model is not uploaded and sandbox has not been defined.

If No is selected for Is data model upload required? option, the Sandbox Location pane displays the list of the Information Domains where data model is uploaded and sandbox has not been defined.

2. Select the required infodom and click . To deselect the infodom select it from the Selected Information Domain pane

and click .Non Time Hierarchy

1. Click in the Non Time Hierarchy toolbar. The Hierarchy Browser displays all the Hierarchies/Filters from each selected Datasets. You can click "+" to expand the node to view the sub levels.

2. Select the required hierarchies and click . To deselect hierarchy by select it from the Selected Members pane and click

.3. Click OK.

Note: Filter conditions are not considered while loading data in to the Hive based sandboxes. To load data with filters, you can use Data Mapping (T2T) to populate the sandbox. Otherwise, you can apply model level filters while modeling.

Table 3. Schema based Sandbox - Field Descriptions

Field Description


http://docs.oracle.com/cd/E60058_01/books/8.0.0.0.0/OFSAAI_User_Guide_8.0.pdf



3. Click Save. The tables of the selected dataset get populated in the sandbox information domain.

Viewing Sandbox Definition

You can view individual Sandbox Definition details at any given point. To view the existing Sandbox Definition details in the Sandbox Definition window, follow these steps:

1. Select the checkbox adjacent to the Sandbox ID.

2. Click in the Sandbox Definition tool bar. The Sandbox Definition View window is displayed with the details such as Sandbox Name, Sandbox Description, Source Dataset, Sandbox Locations, Time, and Non Time Hierarchy and Hierarchical members.

Hierarchy memberClick in the Hierarchy Member toolbar and specify the members or nodes in the Hierarchy browser. You can click "+" to expand the node to view the sub levels.

When you select a hierarchy member, the data belonging to that hierarchy will be copied to the sandbox.

Time Hierarchy1. Click in the Time Hierarchy toolbar. The Hierarchy Browser displays the

available Time Hierarchies which are common across all the selected Datasets. You can click "+" to expand the node to view the sub levels.

2. Select the required time hierarchy and click to move it to the Selected Hierarchies pane. To deselect time hierarchies select from the Selected Hierarchies pane and

click .3. Click OK.

Note: Time hierarchy selection is optional. However, if you do not select a time hierarchy, the following will occur: Time referencing will not be applicable for model definitions. For the model definitions, the variable browser will not show any variable

definitions that has got filters. The shocking of transition matrix multi factor models will not work in

sandboxes For models defined using techniques transition matrix - EWMA, MCEM, MC

simulations, the execution may failHierarchy Member

Click in the Hierarchy Member toolbar and specify the members or nodes in the Hierarchy browser. You can click "+" to expand the node to view the sub levels.

Time hierarchy is used to group data based on time (year or month). When you select time hierarchy member, the data belonging to that time hierarchy will be copied to the sandbox.

Table 3. Schema based Sandbox - Field Descriptions

Field Description



Modifying Sandbox Definition

You can update only the description and Time Hierarchy Member (if it is selected in the definition) of an existing schema based Sandbox Definition. To modify Sandbox Definition, follow these steps:

1. Select the checkbox adjacent to the Sandbox ID whose details are to be updated.

2. Click in the Sandbox Definition tool bar. The Edit button is disabled if you have selected multiple Sandbox IDs.

3. Modify the Sandbox Definition details as required. For more information, see the Creating Sandbox section.

Note: You can edit Time and Non-Time Filters in Sandbox Definition Edit page.

Searching Sandbox Definition

The Sandbox Definition page allows you to search the sandboxes that you want to view.To search for Sandbox, follow these steps:

1. Navigate to the Sandbox Definition page.

Note: By default, the Sandbox Definition page displays all Sandboxes.

2. Enter the name of the sandbox you want to search.

3. Click . The filtered list is displayed.

Sandbox Maintenance

The Sandbox Maintenance window is used to map user groups to the sandbox definition which enables the users to create models in the sandbox.This section includes the following topics:

Accessing Sandbox Maintenance

Requesting and Authorizing to Populate Sandbox

Requesting and Authorizing to Delete Sandbox

Incremental Data Model Upload

Once the Incremental changes are updated to the Data Model in the Sandbox Information Domain, you need to re-save the details in the Sandbox definition window. For more information, see the Modifying Sandbox Definition section.Searching Sandbox Maintenance

Accessing Sandbox MaintenanceYou can access the Sandbox Maintenance page from the Oracle Financial Services Enterprise Modeling Home page.To access the Sandbox Maintenance, follow these steps:







5. Click Sandbox Maintenance from the expanded Navigation list to view the next level of links in the menu. Click Sandbox Maintenance to display the Sandbox Maintenance window.

Figure 9. Sandbox Maintenance Page

Requesting and Authorizing to Populate SandboxNote: 1. 2. The data type Timestamp is not supported during sandbox population.

To request and authorize to populate sandbox in the Sandbox Maintenance window, follow these steps:

1. Select the sandbox which you want to populate and click in the Sandbox Maintenance toolbar. The Edit button is disabled if you have selected multiple checkboxes. The Sandbox Maintenance (Edit Mode) window is displayed.

Note: While populating a Sandbox both Time and Non-Time Filters can be edited in Sandbox Maintenance window.

2. In the Request Action tab, select Complete for Populate Sandbox to copy the required table data from the Production infodom to the Sandbox infodom based on the sandbox definition.

3. Click the Authorize tab, and select the Populate Sandbox - Complete/ Incremental checkbox to authorize sandbox population. This tab is enabled only if your user role is mapped to the function SANDBXAUTH.

4. Click Save to confirm changes. On authorization, a Sandbox-Populate batch is registered in the OFSAA Infrastructure Operations module. The batch will be available in the Batch Scheduling window with the Sandbox ID. This batch must be triggered from the Batch Scheduling window to complete the data population.

Requesting and Authorizing to Delete SandboxTo request and authorize to delete sandbox in the Sandbox Maintenance window, follow these steps:

1. Select the sandbox which you want to delete and click in the Sandbox Maintenance tool bar. The Edit button is disabled if you have selected multiple checkboxes. The Sandbox Maintenance (Edit Mode) window is displayed.

2. In the Request Action tab, select the Delete Sandbox checkbox.

3. Click the Authorize tab, and select the Delete Sandbox checkbox to authorize the deletion. This tab will be enabled only if your user role is mapped to the function SANDBXAUTH.

4. Click Save to remove the Sandbox Definition.



Incremental Data Model UploadThe Increment Upload Required column in the Sandbox Maintenance window displays the status as "Y" or "N" depending on the need for Incremental Data Model Upload for the Sandbox definition. The Incremental Data Model Upload button is enabled only when the model versions between Production and Sandbox information Domain are different.Incremental Data Model Upload facilitates you to synchronize the difference of a Data Model which exists in Production and Sandbox Information Domain. You can refresh the details and fetch the incremental data model changes from Production to Sandbox Information Domain.Note the following points:

The Incremental Data Model Upload button will not be enabled for the sandboxes in which the No option is selected for the Is data model upload required? field, even though there is a difference between the model versions in Production and Sandbox information domains.

Incremental data model upload is supported only when the difference in sandbox and production is at table level, that is, the sandbox schema has at least one table less or more when compared to the production schema.

Incremental data model upload is not supported for Hive based sandboxes.To perform an incremental data model upload, follow these steps:

1. Select the checkbox adjacent to the Sandbox ID which has the Increment Upload Required status as "Y". The status will be Y only when there is a difference in the model versions between Production and Sandbox information Domain.

2. Click from the Sandbox Maintenance tool bar. The Incremental Data Model Upload window is displayed with the selected Sandbox definition details in View mode.

3. Incremental Data Model UploadClick Refresh. The incremental data model changes are fetched and updated.

4. An information dialog is displayed confirming successful incremental data model upload. Click OK.



Once the Incremental changes are updated to the Data Model in the Sandbox Information Domain, you need to re-save the details in the Sandbox definition window. For more information, see the Modifying Sandbox Definition section.Searching Sandbox MaintenanceThe Sandbox Maintenance page allows you to search the sandboxes that you want to view.To search for Sandbox Maintenance, follow these steps:

1. Navigate to the Sandbox Maintenance window.

2. Enter the name of the sandbox you want to search.



CHAPTER 5 Managing Techniques

This chapter provides information about Techniques in the Oracle Financial Services Enterprise Modeling application and step-by-step instructions to use this section.

This section includes the following topics:

About Techniques

User Roles and Functions

Managing Techniques

About Techniques

Technique is a set of generalized statistical algorithms which can be used to build analytical models. The Technique Registration module in Enterprise Modeling displays the techniques developed using R script and ORE functions,.


About TechniquesChapter 5–Managing Techniques

Technique Registration Workflow

Figure 10. Technique Registration Workflow


The following functions should be mapped to the user to manage techniques:

TECHDEF

TECHAUTH


Managing TechniquesChapter 5–Managing Techniques

Managing Techniques

This section explains how to manage Techniques in Oracle Financial Services Enterprise Modeling application.The following topics are included in this chapter:

Accessing Techniques

Creating a Technique Based on Scripts

Authorizing a Technique

Viewing a Technique

Modifying a Technique

Copying a Technique

Deleting a Technique

Searching Techniques

Accessing Techniques

You can access the Technique Registration window from the Oracle Financial Services Enterprise Modeling Home window.To access the Technique Registration window, follow these steps:





2. Click Technique Registration. The Technique Registration window is displayed.

Figure 11. Technique Registration

Creating a Technique Based on Scripts

Oracle Financial Services Enterprise Modeling application enables you to create techniques based on scripts. The supported scripting languages are R or ORE. After registering the techniques, they can be used in model definitions. In the Model Script tab, you can write the script in your preferred language. The Declaration Block in the script is used to declare the input and output parameters. The format of declaration block and the content should not be altered. The scripting should start only from the line after the declaration block.



There are three types of input parameters which can be used in the script, namely Variable, Single Value Parameter, and Model Input. The input parameters can be defined from the Declaration block in the Script Console pane, or by right-clicking the parameter and selecting the Input type. The value of single value parameter is defined from the Inputs window. However, the value of variables and model inputs are mapped during the model definition in which the technique is used.You can configure the numeric output by declaring it in the Declaration block (## Output -->), or by right-clicking the parameter and selecting the Output type. It is not required to declare output parameters for plots or graphical outputs. The script should be a valid script. You can use ORE functions instead of R functions for better performance in R script. All the object names or functions used in the script should be syntactically valid names. Validation of the script is not done while registering the technique. If you already have defined a technique using R script and saved it in a .R file, you can upload it and register it as a technique.To create a technique using scripts, follow these steps:

1. Click Technique Registration from the Navigation list. The Technique Registration window is displayed.

2. Click from the Technique Registration toolbar. The Technique Definition window is displayed.

Figure 12. Technique Definition

3. Enter the details as described in the following table:Table 4. R script based Technique Definition - Field Description

Field DescriptionFields marked in red asterisk (*) are mandatory.Technique Name Enter a name for the technique.

Ensure that there are no special characters like `, {,},", ', ~, <,>, /, \, and multiple spaces.



4. In the Script Console pane, enter the R script for the technique. For more information on how to use fitting and prediction, seethe Fitting and Prediction Using R Script section.

Figure 13. Script Console

5. By default, the Script Console is displayed as shown above. Enter the following details:

## Variable --> Enter the variables you want to use in the R script separated by commas. You should map these variables to the corresponding variables which are available in the Variable Management window, during defining the model.

## Single Value Parameter --> Enter the input parameters you want to use in the R script.

Technique Class Technique Class is used for logically grouping the techniques.

1. Click and open the Technique Class Selection window. 2. Right-click Technique Classes or the technique class name under which you

want to create a technique class, and click Add Technique Class. Enter the technique class name in the Add Node window.

3. Expand the Technique Classes tree, select the required technique class and

click . 4. Remove a selected technique class by selecting the technique class and

clicking .Technique Description Enter a brief description about the technique.Based On Select the Script option.

Language Select the scripting language. The options are:

R MATLAB

Input Data Type Select the input data type.For Hive-based Infodoms, the input data types are Data Frame and HDFS file.For RDBMS based infodoms, the input data typesare RDBMS Data Frame and ORE Frame.

Table 4. R script based Technique Definition - Field Description

Field Description



## Model Input--> Enter the scriptvariable names to which you want to load the output of another model in case of model chaining.

## Output --> Enter the output parameters you want to use in the R script.

## Model Output--> Enter the output object names you want to persist.

Note: The variables and the input parameters are case sensitive. The same name cannot be used for both variable and input parameters.

6. Click from the Script Console toolbar. The Inputs window is displayed.

Figure 14. Inputs window Enter Display Name for the Variables or Model Inputs you defined. The mapping of variables is done

during model definition.

Enter Display Name and Value for single value parameters you defined.

Select the parameter and click to delete it.

7. Click from the Script Console toolbar. The Outputs window is displayed.

Figure 15. Outputs window Enter Display Name for the output parameter you defined.

8. Click to browse and upload .R file. You can use this option if you have already defined a technique and saved it in an R file. Once uploaded successfully, the script is displayed in the script console pane.

9. Select the Include Scripts tab if you want to source already defined algorithms to your script.

Select the required algorithms/ scripts from the Scripts List tree and click .



You can view the scripts by selecting the script and clicking .

After adding scripts, you can position the scripts as required by selecting it and clicking and .

10. Click the Comments tab and click to add additional information, if required.

11. Click Save. If you have authorization privilege, then the technique is auto authorized and it can be used in creating models.

Authorizing a Technique

An authorizer has to authorize a technique before it can be used for model definition. Users who have the function TECHAUTH mapped to their user role can authorize the technique. If the user who created the technique has authorization privilege, then the technique is auto authorized. An authorizer can reject a technique if required. To authorize or reject a technique, follow these steps:

1. Click Technique Registration from the Navigation list. The Technique Registration window is displayed.

2. Select the technique and click . The Authorize Technique window is displayed.

3. Enter a brief description in the Comments field. This is mandatory for rejecting the technique.

4. Click Approve or Reject.

Viewing a Technique

This option allows you to view the details of user defined techniques such as technique name, class, description and the script.To view the technique details, follow these steps:

1. Click Technique Registration from the LHS menu. The Technique Registration window is displayed.

2. Select the checkbox adjacent to the Technique ID and click in the Technique Registration tool bar. The Technique View window is displayed with all the technique details.



Modifying a Technique

You can update the details of a user defined technique. If the technique is already mapped to a model, you cannot modify it. An alert message is displayed. You cannot modify the Technique Name and Technique Class. For techniques based on external library, you can modify External Library Name, function, and parameter details.After modification, the technique should be authorized. If you have authorization rights, it will be auto authorized; else an authorizer has to approve it.To modify a technique, follow these steps:

1. Click Technique Registration from the LHS menu. The Technique Registration window is displayed.

2. Select the technique you want to modify and click . This button is disabled if you have selected multiple techniques.

3. Modify the technique details. For more information, refer to Creating a Technique Based on Scripts section.

Copying a Technique

You can create a copy of a user defined technique with the required variants. The copied technique should be authorized. An authorizer has to approve or reject the copying of the technique. If you have authorization rights, it will be auto authorized.To copy a technique, follow these steps:

1. Click Technique Registration from LHS menu. The Technique Registration window is displayed.

2. Select the technique you want to copy and click . This button is disabled if you have selected multiple techniques.

3. In the Technique Definition Copy window, you can:

Create a new technique with the existing details. Specify a new Technique Name and click Save.

Create a new technique by updating only the required details. Specify a new Technique Name and update the required details.

For more information, see section Creating a Technique Based on Scripts.

Deleting a Technique

You can delete a user defined technique which is no longer required. However, if the technique is already mapped to a model, you cannot delete it. An authorizer has to approve or reject the deletion of the technique. If you have authorization rights, it will be auto authorized. To delete a technique, follow these steps:

1. From the LHS menu, click Technique Registration. The Technique Registration window is displayed.

2. Select the technique you want to delete and click .

3. Click OK in the information dialog to confirm deletion.



Searching Techniques

The Techniques Registration window allows you to search for the techniques that you want to view.To search for Techniques, follow these steps:

1. Navigate to the Technique Registration window.

2. Enter the Technique Name and Technique Class you want to search.



CHAPTER 6 Managing Variables

This chapter provides information about variables in the Oracle Financial Services Enterprise Modeling application and step-by-step instructions to use this section.


About Variable Definition

Managing Variables


About Variable DefinitionChapter 6–Managing Variables

About Variable Definition

Variable refers to a set of attributes that are likely to change based on the selected parameters. In a modeling environment, variables play a vital role in filtering the model parameters and deriving an estimate based on historical data. Variables are defined in the production information domain.

Managing Variables

This section explains how to manage variables in the Oracle Financial Services Enterprise Modeling application.The following topics are included in this section:

Accessing Variable Management

Adding Variable Definition

Viewing Variable Definition

Searching Variables

Modifying Variable Definition

Copying Variable Definition

Deleting Variable Definition


Managing VariablesChapter 6–Managing Variables

Accessing Variable Management

You can access the Variable Management window from the Oracle Financial Services Enterprise Modeling Home page.To access the Variable Management window, follow these steps:





5. Click Data Management from the expanded Navigation list to view the next level of links in the menu. Click Datasets and Variables.

6. Click Variable Definition. The Variable Management window is displayed.

Figure 16. Variable Management

Saving Column PreferencesYou can move the default position of the columns to suit to your viewing preferences. The following is the procedure:

1. Drag and drop a column to a required position in the grid.

2. Click the Save Preferences button to save. Click the Reset button to revert to the default setting.



Adding Variable Definition

The Add Variable Definition feature enables you to add a variable by filtering through the required parameters. The options available to filter the data vary depending on the variable type selected.

Note: The variables created in the Variable Definition window are IDIOSYNCRATIC NUMERIC variables.

To add variable definition, follow these steps:

1. Select from the Variable Management tool bar.

2. The Add button is disabled if you have selected any checkbox in the grid. The Variable Definition window is displayed.

Figure 17. Variable Definition

3. Enter the Variable details as described in the following table:

Table 5. Variable Definition - Field Descriptions

Field DescriptionFields marked in red asterisk (*) are mandatory.Based On Select any of the following options from the drop-down list, based on which the

variable definitions are to be created. Hierarchy Measure Business Processor Entity

Folder Select the folder in which you want to create the variable.Variable Name Enter a name for the variable. Ensure that there are no special characters like `,

{,},", ', ~, <,>, /, \, and multiple spaces.Variable Description Enter the required description for the variable.



Hierarchy The following steps are applicable if you are creating a variable based on Hierarchy:

1. By default, this field displays Direct and is read-only If it is enabled for in your application, select Variable Property as Direct or Dummy (variable in Binary form and used mainly in regression analysis).

2. Select the required Hierarchy from the Selected Hierarchy drop-down list.

3. Click Save.

Note: Hierarchy based variables cannot be created on multi-level Business Intelligence hierarchies.

Measures The following steps are applicable if you are creating a variable based on Measures:

1. Select the Measure from the Selected Measures drop-down list.

2. Create the Variable Transformation. See the description for Variable Transformation in this table for more details.

3. Click Save.

Note: For more details on creating a Business Measure, see Creating Business Measure section in Oracle Financial Services Analytical Applications Infrastructure User Guide.

Business Processor The following steps are applicable if you are creating a variable based on Business Processor:

1. Select the Business Processor from the Selected BPs drop-down list.

2. Create the Variable Transformation. See the description for Variable Transformation in this table for more details.

3. Click Save.

Note:

1. Variables cannot be created based on Business Processor having placeholders.

2. For more details on creating a Business Processor, see Adding a Business Processor section in Oracle Financial Services Analytical Applications Infrastructure User Guide.


Field Description







4. Click Save to upload the new Variable Definition details.

Viewing Variable Definition

You can view individual Variable Definition details at any given point. To view the existing Variable Definition details, follow these steps:

1. Select the checkbox adjacent to the Variable ID.

Entity The following steps are applicable if you are creating a variable based on Entity:If the variable is based on Entity, follow these steps:

1. Click to open the Entity and Attribute window. The available tables are displayed under the Available Entities grid.

2. Select the checkbox corresponding to the table that you want to select. The available columns in the selected table are displayed under the Available Attributes grid.

3. Select the required attribute for which you want to create a variable. You can select multiple attributes.

4. Click Save. The selected attributes are displayed in the Selected Attributes grid.

5. Click Save on the Variable Definition window. Variable Transformation This feature is available for variables based on Measures, Business Processor,

and Entity.

1. Click to define a transformation for the Variable. The Technique Browser for Transformation window is displayed.

2. Select the transformation you want to apply to the variable and click . The options are Arithmetic, Exponential, Logarithm, Power, and Trigonometric. If Arithmetic is selected, select the Operator (Addition, Subtraction,

Multiplication, and Division) from the drop-down list and enter the Operator Value.

If Logarithm is selected, select the Base from the drop-down list. The options are 10, 2, and e.

If Power is selected, enter the value of Power. If Trigonometric is selected, select the Function from the drop-down list.

The options are Sine, Cosine, and Tangent. If Exponential is selected, the exponential value of the variable will be

considered for transformation of the variable.

Select an applied transformation and click to remove its associa-tion to the variable.

3. Click OK.


Field Description



2. Click in the Variable Management tool bar. The Variable Definition View window is displayed with all the variable details.

Searching Variables

The Variable Management window allows you to search for the variables that you want to view.To search for Variables, follow these steps:

1. Navigate to the Variable Management window.

2. Enter the name of the Variable Name you want to search.

3. Click .The filtered list is displayed.

Modifying Variable Definition

You can modify only the Variable Description details if the variable is mapped to a definition. If the variable is not mapped to any definition, then all the filtering parameters can be modified. To modify an existing Variable Definition, follow these steps:

1. Select the checkbox adjacent to the Variable ID whose details are to be updated.

2. Click in the Variable Management tool bar. The Edit button is disabled if you have selected multiple Variable IDs. The Variable Definition Edit window is displayed.

3. Edit the Variable Definition details as required. For more information, see section Adding Variable Definition.

4. Click Save to upload changes.

Copying Variable Definition

The Copy Variable Definition page enables you to quickly create a new Variable based on the existing variable parameters or by updating the required parameters. To copy an existing Variable Definition, follow these steps:

1. Select the checkbox adjacent to the Variable ID whose details are to be duplicated.

2. Click in the Variable Management tool bar. The Copy button is disabled if you have selected multiple Variable ID(s).

3. In the Variable Definition Copy window, you can:

Create new variable with the existing details. Specify a new Variable Name and click Save.

Create new variable by updating only the required details. Specify a new Variable Name and update the required details. For more information, see section Adding Variable Definition. Click Save. The new variable definition details are displayed in the Variable Management window.



Deleting Variable Definition

You can remove the variable definition(s) which are no longer required in the system, by deleting from the Variable Management window. You cannot delete a variable which is mapped to any definition.To delete a Variable Definition, follow these steps:

1. Select the checkbox adjacent to a Variable ID.

2. Click from the Variable Management tool bar. A confirmation dialog is displayed.

3. Click OK to confirm deletion.


CHAPTER 7 Managing Modeling

This chapter provides information about Modeling in the Oracle Financial Services Enterprise Modeling application and step-by-step instructions to use features in Modeling.


About Modeling

Managing Modeling

Model Chaining


Model Outputs

Model Deployment

Model Execution


About Modeling

Modeling refers to the process of designing a prototype based on a structured data model, considering all the variables for statistical analysis and to simulate real events and processes. Modeling module enables you to measure and quantify risk. You can use pre-defined models to predict the business trends and also to validate the existing models.Models are built based on various techniques and related parameters based on the business purpose. Models thus built use various metadata objects such as Measures, Hierarchies, Filters, Datasets, and so on. The techniques can be inbuilt techniques and user defined script based techniques. Oracle Financial Services Enterprise Modeling application supports developing statistical models using algorithms written in standard R. It also enables you to bring in already developed algorithms written in R for creating models based on them. Thus it provides a unified environment to build, host and execute models across risk categories and a common framework to manage stress scenarios.


About ModelingChapter 7–Managing Modeling

Modeling Workflow

Figure 18. Model Creation Workflow


Managing ModelingChapter 7–Managing Modeling


Functions required to be mapped to the user for managing models are as follows:

MDLDEF

MDLAUTH

MDLEXEC

MDLOUTPUT

MDLDEPLOY

MDLCALIB

MDLCHAMP

Managing Modeling

This section explains how to manage modeling feature in Oracle Financial Services Enterprise Modeling application.The following topics are included in this section:

Accessing Model Management

Model Versioning

Creating and Executing a Model

Model Parameters

Viewing Model Definition

Modifying Model Definition

Deleting Model Definition

Searching Model Management

Accessing Model Management

You can access the Model Management window from the Oracle Financial Services Enterprise Modeling Home page.To access the Model Management window, follow these steps:


2. Click from the header to display the sandboxes in a Tiles menu.

3. Select the required Sandbox from the Tiles menu. The Navigation list to the left displays the link for Modeling. Click Modeling to expand the list.

4. Click Model Creation. The Model Management window is displayed.



Figure 19. Model Management

Note: The latest version of the model definition is displayed, when you select a model. You can click to view the Model Definitions and click to edit the Model Definition.

Model Versioning

The Model Definitions created in Oracle Financial Services Enterprise Modeling are versioned. The version of the Model

Definition is displayed in the Model Management window. Select a model and click to view different versions of the selected Model Definition. When a Model Definition is created, it is saved as version 0. During subsequent modifications, the Model Definition is saved as different versions. For example, the version of a Model Definition is 0, after it is created. The version is updated to 1, after the Model Definition is edited. After editing a Model Definition, you can choose to save a Model Definition as a newer version or overwrite the existing version.

Modeling using Open R with HDFS Option

Oracle R Advanced Analytics for Hadoop (ORAAH)/Oracle R Connector for Hadoop (ORCH) is the default approach for running Open-R on HDFS. For the configurations required for creating and executing models in Open R framework with HDFS option, see Configurations for Open-R with HDFS section in OFSAAI Administration Guide available at OHC Documentation Library.

Basic FunctionsUse the following basic functions to get basic OFSAA Metadata details, basic Model details and basic Run details:

ofs_get_log_handle()ofs_get_model_id()ofs_get_model_version()ofs_get_model_name()ofs_get_batch_run_id()ofs_get_task_id()ofs_get_infodom()ofs_get_dataset_codes()ofs_get_dataset_names()ofs_get_dataset_queries()ofs_get_output_names()ofs_get_meta_data_info()


http://docs.oracle.com/cd/E60058_01/PDF/8.0.0.x/8.0.0.0.0/OFSAAI_Administration_Guide_8.0.pdf


ofs_get_hive_schema_name()

Examples: model_id = OFSAAIRunnerHDFS::ofs_get_model_id();batch_run_id = OFSAAIRunnerHDFS::ofs_get_batch_run_id();meta_data_list = OFSAAIRunnerHDFS::ofs_get_meta_data_info();

ofs_get_meta_data_info() is a special function for getting dataset and variable and its mapping info. This function returns a list for each of the dataset. Sample list for a single dataset looks like as given:

variable.names - contains all the script variables mapped for the dataset.variable.index - contains the index of each selected variable in the dataset query.variable.entity.attributes - contains actual base entity and attributes for the selected mata-data variable.dataset.base.entities - contains all the base entities part of the dataset.dataset.query - contains dataset query for fetching data.

Note: All these attributes are available as vectors within the list.

print( meta_data_list );$`Dataset_Name`$`Dataset_Name`$variable.names[1] "x" "y" "z"

$`Dataset_Name`$variable.index[1] "1" "2:3" "4"

$`Dataset_Name`$variable.entity.attributes[1] "DATASET_TABLE_1.DATASET_COLUMN1" "DATASET_TABLE_2.DATASET_COLUMN2" "DATASET_TABLE_2.DATASET_COLUMN3" "DATASET_TABLE_3.DATASET_COLUMN4"

$`Dataset_Name`$dataset.base.entities[1] "DATASET_TABLE_1" "DATASET_TABLE_2" "DATASET_TABLE_3"

$`Dataset_Name`$dataset.query[1] "Dataset Query"

Note: For multiple datasets, preceding lists get appended.

ofs_get_log_handle() : returns, handle to an existing log handle.ofs_get_model_id() : returns, ID of the Model, as character/string.ofs_get_model_version() : returns, Version of the Model, as numeric.ofs_get_model_name() : returns, Name of the Model, as character/string.ofs_get_batch_run_id() : returns, Batch Run Id of the Current Batch, in which model is added for the execution, as character/string.



ofs_get_task_id() : returns, Task Id of the Model in current Batch, character/stringofs_get_infodom() : returns, OFSAA information Domain (Infodom ) Name, as character/string.ofs_get_dataset_codes() : returns, Dataset-Codes for all the selected datasets, as character vector.ofs_get_dataset_names() : returns, Dataset-Names for all the selected datasets, as character vector.ofs_get_dataset_queries() : returns, Dataset-Queries for all the selected datasets, as character vector.ofs_get_output_names() : returns, selected Output names, as character vector.ofs_get_hive_schema_name() : returns, Hive schema name, as character/string.

ORAAH Based Functions for Spark/OREFollowing framework functions will help in connecting/disconnecting Spark/ORE(Hive). Internally these functions will use ORAAH for Spark/ORE connections. These functions will return TRUE on success and FALSE in case of any failures with the proper error message in the logger.

ofs_ORAAH_spark_connect()ofs_ORAAH_spark_disconnect()ofs_ORAAH_hive_connect()ofs_ORAAH_hive_disconnect()

All the 4 functions returns -TRUE : on SuccessFALSE : on Failure

Note: ofs_ORAAH_spark_connect/ofs_ORAAH_spark_disconnect should be called first/last , before using any ORCH-Spark related function, like orch.glm2(). ofs_ORAAH_hive_connect/ofs_ORAAH_hive_disconnect should called first/last, before using any ORCH-ORE related functions.

RDBMS Connect Functionsofs_rdbms_connect( arg.ofsaa.schema.name = c( "CONFIG", "ATOMIC" ) );ofs_rdbms_disconnect( arg.ofsaa.schema.name = c( "CONFIG", "ATOMIC", "BOTH" ) )These functions should be used to get RDBMS connections/disconnections. OFSAA schema type should be passed as an argument. By default it connects to Configuration Schema.

ofs_rdbms_connect( arg.ofsaa.schema.name = c( "CONFIG", "ATOMIC" ) );returns -

valid connection handle object in case of success.NULL in case of failure with proper error message in logger.

ofs_rdbms_disconnect( arg.ofsaa.schema.name = c( "CONFIG", "ATOMIC", "BOTH" ) )returns -

TRUE : on SuccessFALSE : on Failure

Example:atomic_conn_handle = OFSAAIRunnerHDFS::ofs_rdbms_connect(); #by default configuration schema.atomic_conn_handle = OFSAAIRunnerHDFS::ofs_rdbms_connect( arg.ofsaa.schema.name = "ATOMIC" );



disconnect_status = OFSAAIRunnerHDFS::ofs_rdbms_disconnect( arg.ofsaa.schema.name "BOTH" );

UsageAll these functions are available as part of OFSAAIRunnerHDFS R package. This package is available under $FIC_DB_HOME/lib as part of installation. This package needs to be installed on a machine which is running Rserve or client R Engine. Package reference should be used in order to avoid conflict with similar function name.

Example: OFSAAIRunnerHDFS::ofs_get_model_id(); OFSAAIRunnerHDFS::ofs_ORAAH_spark_connect();

Creating and Executing a Model

The Modeling module enables you to create and execute data models. This section has the following topics:

Creating a Model Using R Scripted Technique

Creating a Model using Script

Note:

1. You can create models only in Sandbox Information Domain.

2. Modeling supports derived entities. However, materialized view is not created in the Sandbox schema during Sandbox creation and you have to do it manually. Get the materialized view definition details from the production schema and create the materialized view in the Sandbox.

Creating a Model Using R Scripted TechniqueNote: You can create models using a technique which you have defined from the Technique Registration window. Execution Status Log is not displayed in Model Definition window for models created using Standard R Engine.

To create a model using R scripted technique, follow these steps:



1. Select from the Model Management tool bar. The Model Definition window is displayed. The Add button is disabled if you have selected any Model ID in the grid.

Figure 20. Model Definition page

2. Enter the Model Definition Details. The common fields are described in Table 6.

The grid below the Model Details section displays the various tabs available for the selected technique. To update the required information, see the following sections:

Inputs

Filter

Outputs



Table 6. Model Definition - Field Descriptions

Field DescriptionFields marked in red asterisk (*) are mandatory.Model Name Specify a model name for the model definition. Model Name is case sensitive and

does not allow duplication. For example, model name "Linear Regression" is not allowed if a model with the name "linear regression" exists.Ensure that there are no special characters like `, {,},", ', ~, <,>, /, \, and multiple spaces.

Model Description Enter a description for the model.Do you like to script the model? Select the checkbox to script the model in Model Script pane.Model Objective

Select the Model Objective from the drop-down list. You can also click to create a Model Objective.

Technique This field is disabled if you have selected to script the model.

1. Click and open the Technique Selection window. The pre-packaged techniques, and user defined (registered and authorized) R techniques are listed in the Techniques pane.

2. Click and expand the technique heading groups.

3. Select the required technique and click . 4. Click OK. The selected Technique details are displayed in the Model Definition

window.

If you have selected R technique, click to view the script.



4. Click Save to save the model definition details, after all the necessary details are updated.

5. Click Preview Data to view the data of the selected dataset. It displays the primary keys and the attributes/ columns of the tables in the selected dataset. Only those columns which are mapped to the variables in the script are displayed.

Note: In case of Hive based Sandbox information domain, previewing data takes long time and only 100 records are displayed.

6. Click Execute.

Dataset By default, the dataset of the Sandbox is displayed. You can change the dataset if necessary. Dataset selection is mandatory: For models based on R scripted techniques if variables are declared in the R

script.

1. Click to open the Dataset Selection window. The available datasets are listed in the Datasets pane.

Select a dataset and click to view the details of the selected datasets.

Click to create a new dataset. For more details on creating a dataset, refer Creating Data Set section in Oracle Financial Services Analytical Applications Infrastructure User Guide. You can create dataset using any of the tables which is part of the production information domain. But if you create a dataset with a table which is not part of the Sanbox and create a model using that dataset, then deploy the model to production infodom and execute it there.

2. Select the required Dataset based on which the model is to be created and click

. Ensure the selected dataset is loaded with data, otherwise model execution will fail. You can select multiple datasets for models executed using Standard R Engine. If multiple datasets are used, you should use atleast a variable from each dataset.

3. Click OK.Note: The Datasets based on Derived Entities are not supported.

Language This field is not displayed for techniques based models.Select the scripting language from the drop-down list. The options are: R Input Data Type

Type This field is not displayed for techniques based models.Select the type of engine from the drop-down list. The options are: Standard R Engine ORE Engine- This option is not displayed in Hive based Infodoms.

Input Data Type This field is displayed only in Hive based infodoms for models based on R scripted techniques or if you select to script the model. Select the input data type. The options are ORE Frame, Data Frame and HDFS File.

Table 6. Model Definition - Field Descriptions

Field Description





The Execution Status grid displays the model execution log dynamically.

Note: For R based models, the execution may fail if the dataset contains internal joins. Executing a model using standard R engine with the new cloudera jars is failing with model queries exceeding a certain limit. Workaround is to append UseNativeQuery=1 in the JDBC URL of the Hive schemas in which model definitions and executions happen. For example, jdbc:hive2:10.123.456.78:1000/default;useNativeQuery=1

Creating a Model using ScriptThis feature allows you to create models based on user written scripts. The supported scripting languages are Standard R and ORE. The script should be a valid script.In case of R script, all the object names used in the R-script should be syntactically valid names. You should select Language as ORE if you are using ORE functions in the script. ORE enables transparent access to data stored in the Database for scalability and high performance. For more information, see Enterprise Modeling support for R Scripts and Oracle R Enterprise (ORE) Statistical Functions.

Note: The models defined based on R scripting can be directly deployed to the production infodom without execution in sandbox.

To create a model using script, follow these steps:

1. Select from the Model Management tool bar. The Model Definition window is displayed.



Figure 21. Model Definition

2. Enter the model definition details. The common fields are described in Table 6.

3. For R scripted model, click to browse and upload .R file. You can use this option if you have already defined a technique and saved it in an .R file. Once uploaded successfully, the script is displayed in the Model Script pane.



Or In the Model Script pane, enter the script for the model. For information on how to use fitting and prediction, see Fitting and Prediction Using R Script.

Figure 22. Scripting Console

Note: The format of declaration and the content should not be altered. The scripting should start only from the line after the declaration block.

By default, the Model Script is displayed as shown above.

## Variable --> Enter the variables you want to use in the script separated by commas.

## Single Value Parameter --> Enter the input parameters you want to use in the script.

## Model Input --> Enter the script variable names to which you want to load the output of another model in case of model chaining.

## Output --> Enter the output parameters you want to use in the script.

## Model Output--> Enter the object names you want to persist.

Note: The variables and the input parameters are case sensitive. Same name cannot be used for both variables and input parameters. You should explicitly define the outputs in the script that need to be updated back to tables. If the output is not defined, then the tables are not updated. For example : x = rbeta(100,1,2) y = rbeta(100,1,2) op = lm(y~x) If op$residuals needs to be updated then declare the output with a name. For Example: Residuals = op$residuals

The grid below the Model Details section displays the various tabs available. To update the required information, see the following sections:

Inputs

Filter

Include Scripts

Outputs



4. Enter valid script of the model in the selected scripting language. For more information on data usage and data handling when R script and ORE functions are used, see R Scripts and Oracle R Enterprise (ORE) Statistical Functions.

5. Click Save to save the model definition details, after all the necessary details are updated.

6. Click Preview Data to view the data of the selected dataset. It displays the primary keys and the attributes/ columns of the tables in the selected dataset. Only those columns which are mapped to the variables in the script are displayed.


7. Click Execute to execute the model.

You can view the status of the model execution dynamically in the Execution Status grid.

Note the following points about Model execution:

For R based models, the execution may fail if the dataset contains internal joins.

Executing a model using standard R engine with the new cloudera jars is failing with model queries exceeding a certain limit. Workaround is to append UseNativeQuery=1 in the JDBC URL of the Hive schemas in which model definitions and executions happen. For example:jdbc:hive2:10.123.456.78:1000/default;useNativeQuery=1

Models executed using Standard R engine will fail if multiple primary nodes are set in the ModelingFramework.XML

Users who are using ORE1.4.1 and R 3.1.1 may have to explicitly set session timezone in scripts registered within OFS EM by using the 'Sys.env(TZ=<time zone>)' R function. Alternatively you may set the same call in 'R_HOME/etc/Rprofile.site' file on the database server, where R_HOME is the home directory of the R instance on which ORE server packages are installed.

8. Click Output to view the output. For more information, see the View Model Outputs section.



Model Parameters

The grid in the Model Details section displays the various parameters applicable when a technique is selected. It is mandatory to update the required information in the tabs displayed and the parameters or fields in the available tabs vary depending on the technique selected. Few of the common input parameter types are explained below.

FilterIn the Filter tab, you can add multiple non-time hierarchy members as filters.

1. In the Model Definition window, click from the Filters toolbar. The Filter Browser window is displayed.

2. Select Data Filter or Hierarchy to display the corresponding filters.

3. Select the required filter or hierarchy from the list and click . The selected filter is added to the Selected Members pane.

Note: If a Hierarchy is created on a table with the same column holding hierarchy Code and Description, then the hierarchy nodes will not be displayed in the Hierarchy Browser window on Hive connections. In such scenario, use the concat functionality in the Description field like concat(<TableName.ColumnName>). For example, concat(credit.age,").

4. Click OK. The selected Filters are displayed in the grid.

5. Filters can be applied to both Production and Sandbox Information Domains. Select the required option Apply in Production or Apply in Sandbox to apply the filter in the required information domain.

Note: Data Element Filters created in the Filters screen is displayed when you select Data Filter.

InputsThe following sections describe the various inputs for:

For Script based Models and For R scripted Technique

For Script based Models and For R scripted Technique

The variables and model inputs you have declared in the Model Script pane are displayed in the Inputs tab.

1. From the Variables grid, map the configured script variables to the available variables in the system.

Select a variable from the Available Variables pane by expanding the required variable type and a variable in the Configured Script Variables pane, and click Map.

You can add a new variable and then use that variable for mapping.

Click to add new variables. The Variable Defintion page is displayed. You can create a new vari-able from the Model Definition page, as per the requirement. For more information, see Adding Vari-able Definition section.

The mapping details are displayed in the Variable-Mapping grid. To unmap, select the variable from the Configured Script Variables pane and click Unmap.

Note:



You cannot select a variable in the Available Variables pane which is already mapped to another variable in the script. That is, an OFSAA variable can be mapped to only one input variable in the model script. If you want to use the same OFSAA variable for mapping to multiple model input variables, create a copy of the variable and assign it to required model input variables.

Mapping of single variable declared in the R script to multiple variables is not supported if ORE implementation is used.

2. From the Model Inputs grid, map the output of the model which you want to map to the declared Model Input.

Select a Model Output from the Available Model Outputs pane by expanding the required model and a Model Input from the Model Inputs pane, and click Map.

The mapping details are displayed in the Configured Model Inputs grid. To unmap, select the Model Input from the Model Inputs pane and click Unmap.

3. From the Input Parameters grid, select the Evaluation Type from the drop-down list. You can execute model on complete set of records in the dataset, on a group of records or on a set of rows.

Group- Select this option if the model needs to be executed on a group, which is defined using a grouping variable. Select the Grouping Variable from the drop-down list.

Row- Select this option if the model needs to be executed on a set of rows. Specify the Number of rows.

Note: For Evaluation Type as Row, graphical output (plot) is not supported and the number of records processed is five times the value given in the Number of Rows field. These are limitations.

NA- Select this option if the model needs to be executed on complete set of records in the dataset.

4. All single value parameters declared in the technique are displayed. Displays the Value if it was given while defining the technique. Else enter the appropriate value.

Include ScriptsThis tab is used to source already defined algorithms to your script.

1. Select the required algorithms/ scripts from the Scripts List tree and click .

2. You can view the scripts by selecting the script and clicking .

3. After adding scripts, you can position the scripts as required by selecting it and clicking and .

OutputsThe following sections describe the various Output options for:

For R Scripted Technique and For Script Based Models

For R Scripted Technique and For Script Based Models

The Outputs tab allows you to store the output values computed during the model execution to a table in your atomic schema by mapping the output value to a variable, which you have defined through the Variables Definition window. This is supported only for record level outputs. Additionally, you can create a new table to store the output if it is a dataframe, vector or matrix.



Note: You can view the Outputs tab only after declaring Output variables (## Output -->) in the model script.

Figure 23. Outputs window

1. From the Outputs tab, click corresponding to the declared output variable whose value you want to map to a variable. The Variable Browser window is displayed.

2. Select the appropriate variable and click Ok.

3. Select the checkbox corresponding to Store in New Table if you want to create a new table to store the output. New table name is <<Outputname>>_<<ModelID>>_<<Version Number>>. The combined length should not be more than 30. So ensure the Output name does not have any special characters.

Note the following limitations:

Creating new tables for storing the model outputs is supported only for scripted models, executed using ORE engine. New table creation may not work for Evaluation type selected as Group and Row.

Note: For models executed using ORE engine, you can map only row level outputs to variables.

Scenarios

Consider the following script where outputs are not configured. Then the framework output will be just the graphical output. That is, when the outputs are not chosen by the user, then the framework reports the last line's output as the only default output. Note that the plots are always produced.## ---------DECLARATION------------## Variable --> DepVar,IndepVar1,IndepVar2## Single Value Parameter -->## Output --> ## --------------------------------art.mod<-lm(DepVar ~ IndepVar1 + IndepVar2)

art.summ<-summary(art.mod)coef( art.summ )art.summ[[ "r.squared" ]]

#do some line plots#plot()

#form new data.frame objectnew.x.datafrme = data.frame(x=seq(from=range(IndependentVariable1)[ 1 ],to=range( IndependentVariable1)[2],length=length(DependentVariable)))

#form new regression model RegModel<-lm(DepVar ~ new.x.datafrme$x + IndepVar2)



#perform some operations on the new dataPlot(RegModel)

In this case, the output window will have a tree containing the components of 'RegModel' object with the same name as the root node.Alternatively, you can also put the intended objects name at the last line of the script as:## ---------DECLARATION------------## Variable --> ## Single Value Parameter -->## Output --> RegModel## --------------------------------art.mod<-lm(DepVar ~ IndepVar1 + IndepVar2)




#form new regression model RegModel<-lm(DepVar ~ new.x.datafrme$x +IndepVar2)

#perform some operations on the new dataPlot(RegModel)RegModel

Here the output window will show a tree containing the components of 'RegModel' object, but since the output name was not configured or specified, it will be held under a generic framework name 'OFSAAOutput'One of the added advantages of configuring the outputs is that one can get more than one object as output as shown in the following script:## ---------DECLARATION------------## Variable --> ## Single Value Parameter -->## Output --> RegModel, art.mod## --------------------------------art.mod<-lm(DepVar ~ IndepVar1 + IndepVar2)






#form new regression model RegModel<-lm(DepVar ~ new.x.datafrme$x +IndepVar2)

#perform some operations on the new dataPlot(RegModel)

For the above script, the output window will contain trees for both 'RegModel' and 'art.mod'. Here are the possible scenarios and the respective framework behavior:

Case A: When no outputs are specified in the declaration block and outputs are not configured in the Outputs window: The value of the last evaluated expression is returned with the name 'OFSAAOutput' for the model execution.

Case B: When no outputs are specified in the declaration block and the Configure Outputs button is clicked: All the objects from the script execution environment get listed as a tree with each object as separate nodes. Now you can choose the outputs to be reported. The selected ones will be placed in the declaration block. If you do not select any object as output, then the behavior will be the same as case A, for the model execution.

Case C: Some objects are declared as outputs in the declaration block either by explicitly writing the correct names in the declaration block against the label Outputs, or by right-clicking a valid object name and selecting it as output: Now, only those which are listed in the declaration block will come in the Configure Outputs window (as checked ones, since you have already selected them as the outputs explicitly).

Viewing Model Definition

You can view individual Model Definition details at any given point. To view the existing Model Definition details in the Model Definition window, follow these steps:

1. Select the checkbox adjacent to the Model ID.

2. Click in the Model Management toolbar. An information dialog is displayed indicating that view mode is selected and any accidental changes will not be updated. Click OK to confirm and view the details.

3. In the Model Definition View window you can:

Note: Select Preview Data to view the variable parameters corresponding to the technique selected.In case of Hive based Sandbox information domain, previewing data takes long time and only 100 records are displayed.

Select Execute to process the model execution. The status of which can be verified in Model Execution Status option, accessing Oracle Financial Services Enterprise Modeling > Modeling > Model > Model Execution Status.



Modifying Model Definition

You can update the model definition details of an existing Model in the Model Definition window:

Note: Modifying ORE models is not supported. Additionally, applying hierarchy filters for R scripted models in edit mode is not supported.

To modify model definition

1. Select the checkbox adjacent to the Model ID whose details are to be updated.

2. Click in the Model Management tool bar. The Edit button is disabled if you have selected multiple Model IDs.

3. Edit the Model Definition details as required. Model Name, Technique, and Model Objective are not editable. You can update the Model Description, Dataset, and the variable parameters based on the technique selected. For more information, see Creating and Executing a Model.

4. Once you have updated all the necessary details in the Model Definition Edit window, you can:

Click Preview Data to view the new Model Definition details before upload.


Click Save to update the model definition details. You can choose save a Model Definition as a new version or overwrite the existing version. For more details, see Model Versioning section.

Click Save and select Execute to process the model execution. The status of which can be verified in Model Execution Status option, accessing Oracle Financial Services Enterprise Modeling > Modeling > Model > Model Execution Status.

Deleting Model Definition

You can remove the model definitions which are no longer required by deleting from the Model Management window.

1. Select the checkbox adjacent to the Model ID(s) whose details are to be removed.

2. Click in the Model Management tool bar.


Searching Model Management

The Model Management page allows you to search the models that you want to view.To search for models, follow these steps:

1. Navigate to the Model Management page.

2. Enter the Model Name and Model Objective.



Model ChainingChapter 7–Managing Modeling

Model Chaining

Model Chaining is used to input an object/objects from one model to another model. It can also be used for separation of calibration and prediction. The objects persisted from any model can be chained to any other model. In case of ORE intermediate objects are saved in ORE data-store whereas in Standard-R / Open-R, objects are saved or persisted as a file.The following tags are available in the Model Script Console:

##Model Input--> Enter the object names to which you want to map object from some other model.

##Model Output--> Enter the object names you want to persist.

Note: Object persisted out of an ORE based model cannot be chained /inputted to Open-R / Standard-R based model and vice-versa.

Figure 24. Model Script Console


Model Execution Status in the Sandbox Information Domain enables you to verify the status of the executed model at any given point. The status can be Complete, Ongoing, or Failed based on the current processed stage of the system.This section includes the following topics:

Accessing Model Execution Status

Searching Model Execution Status

Accessing Model Execution Status

To access the Model Execution Status page, follow these steps:

1. Click Sandbox tab.

2. Click Model Execution Status. The Model Execution Status page is displayed.


Model OutputsChapter 7–Managing Modeling

Note: This menu is displayed only if you are in sandbox infodom.

Figure 25. Model Execution Status

Searching Model Execution Status

The Model Execution Status page allows you to search the models that you want to view.To search for Model Execution, follow these steps:

1. Navigate to the Model Execution Status page.

2. Enter the Model Name.


Model Outputs

The Model Outputs page in Sandbox Information Domain displays the outputs of all models executed in the selected Sandbox Information Domain. The Model Outputs page in Production Information Domain displays the outputs for the models deployed in the production Information Domain and also those requested for execution. You can also view or download the derived output values as a .csv file and plots as .png files for reference.Limitation: You cannot download the CSV file for ORE Model Output if data redaction is enabled.This section includes the following topics:

Accessing Model Outputs

Searching Model Outputs

View and Compare Model Outputs

Accessing Model Outputs

You can access the Model Outputs page from the Sandbox.To access the Model Outputs page from Sandbox, follow these steps:






4. Click Model Output. The Model Outputs page is displayed.

Figure 26. Model Outputs

Searching Model Outputs

The Model Outputs page allows you to search the models that you want to view.To search for Model Outputs, follow these steps:

1. Navigate to the Model Outputs page.



View and Compare Model Outputs

To view and compare the model outputs, follow these steps:

1. Select the checkbox adjacent to the Model ID.

Click from the Model Outputs toolbar. The Output Summary window is displayed.

Figure 27. Outputs Summary

2. Select the checkbox adjacent to a Batch Run ID to view the details or select multiple checkboxes to compare Batch Run IDs.



3. Click to view details or compare Batch Run ID(s). The View/Compare Sample window is displayed with the comparison details of the selected models.

4. Click the links to view/download the derived values stored as .csv files or plots saved as image files.

Figure 28. Output window for R scripted models



5. Click the file name (.dat file) under the Summary Output pane to view the summary of outputs. This is displayed only for models based on Standard R Engine.

6. Click the links in the Outputs pane to download or view the derived values in .csv files.


Model DeploymentChapter 7–Managing Modeling

Model Deployment

Model Deployment in the Sandbox Information Domain enables you to request for deployment of a model to the production information domain. Once you define a model and test it in the Sandbox, and if the testing (checking the results of model execution) results in concluding that the definition is sound and satisfactory, promote the model to the production infodom to execute the model on the production data.Since models based on R scripting can be directly deployed without execution, those models will be displayed in the Model Deployment window. This section includes the following topics:

Accessing Model Deployment

Searching Model Deployment

Requesting and Authorizing Model Deployment

Accessing Model Deployment

To access the Model Deployment page, follow these steps:



3. Click Model Deployment to display the Model Deployment window.

Note: This menu is displayed only if you are in sandbox infodom.

Figure 29. Model Deployment Page

Searching Model Deployment

The Model Deployment page allows you to search the models that you want to view. To search for Model Deployment, follow these steps:

1. Navigate to the Model Deployment window.



Model DeploymentChapter 7–Managing Modeling


Requesting and Authorizing Model Deployment

You can request a model for deployment and view its status in the Processing Modeling Framework (PMF). You can view the current status of the model deployment life-cycle and click on the various symbols in the PMF diagram to view details for the specific state in the workflow.

Note: For more information on PMF, see the OFSAAI User Guide Release 8.0.9.0.0 on OHC.

To request and authorize a model for deployment, perform the following steps:

1. Select the checkbox adjacent to Model ID and click to display the Model Deployment Authorization window.

2. Select the Request for Deployment checkbox in the Model Deployment grid.

Note: Once you have requested for deployment, you can view the status for the model deployment in the PMF. Click from the header to display the Administration tools in Tiles menu. Click Processing Modeling Framework from the Tiles menu to view a list of links. Click Process Monitor from the list and search for the Model that was requested for deployment. Click the link to display the PMF window. The window displays the state of the Model deployment.

3. Select Authorize from the Authorize and Deploy checkbox to authorize the deployment request. This radio button is enabled if you are mapped to the MDLAUTH function role. If you are not mapped to the role, an authorizer mapped to the role has to approve the deployment.

Note: Click the My Inbox link and then click My Task from the Navigation list. This link is available on the menu if your administrator has configured the required privileges to your user profile in the system. You can view notifications for the approval or rejection of the Model deployment here. For rejected requests, you can click the Sandbox icon and edit the model from the Model Creation window. For more information, see Creating and Executing a Model. Once you have edited the Model, you can request again for approval to deploy and the process is a repeat of steps 1 to 3. Authorizers approving or rejecting model deployment requests can access from My Inbox > My Task as described previously. Click Task ID in the notification to display the Model Deployment Authorization window and approve or reject the model deployment.

4. Click Save to process the model deployment.On successful deployment, the model is available in the Run Rule Framework as a component. Run filter and process filter is supported for models stitched in a Run for execution.

Note:

To execute Open R models, pass the value for $HOST as a Run-Time parameter.

Note:

Once deployed, you can view the success or failure of the Model deployment in the PMF window.

Click from the header to display the Administration tools in Tiles menu. Click Processing Modeling Framework from the Tiles menu to view a list of links. Click Process Monitor from the list and search for the



Model ExecutionChapter 7–Managing Modeling

Model that was deployed. Click on the link to display the PMF window. The window displays the state of the Model deployment as either Successful or Failed. You can click on the symbols to view more details. The following screenshot of the Model deployment on the PMF window is an example that has been through a cycle of model rejection and approval, and deployment failure and success.

Model deployment states on PMF window

Model Execution

The Model Execution module allows to you request for model execution and work with champion challenger.This section includes the following topics:

Request for Model Execution

Champion Challenger

Request for Model Execution

Request for Model Execution in the Production Information Domain enables you to request for a batch execution for the selected model. A batch is scheduled in the Batch Processing and the model is executed in the production environment.This section includes the following topics:



Accessing Request for Model Execution

Requesting for Model Execution

Searching for Request for Model Execution

Accessing Request for Model ExecutionTo access the Request for Model Execution page, follow these steps:





5. Click Model Execution and then click Process Management to view the menu.

6. Click Model Management and then click Request for Model Execution. The Request for Model Execution window is displayed.

Figure 30. Request for Model Execution

Requesting for Model ExecutionTo request for model execution, follow these steps:

1. From the Request for Model Execution window, select the required model and click . The Request for Model Execution window is displayed.

2. Select the Register Batch checkbox.

3. Click Save to process for model execution.When there is a request for model execution, a batch is registered in the Operations framework which has to be executed from the Batch Scheduling window in the application. For more information, see Operations section in the Oracle Financial Services Analytical Applications Infrastructure User Guide.

Note: The Request for Model Execution page displays all deployed models in case of schema based sandboxes.

Note: Market Risk (MR) Models will not be displayed on the Request for Model Execution window. To create and execute a MR Model, go to the Run Definition window.

Searching for Request for Model ExecutionThe Request for Model Execution page allows you to search specific model execution request.To search for Request for Model Execution, follow these steps:



1. Navigate to the Request for Model Execution page.



Champion Challenger

Champion refers to the first model deployed, after calibrating the parameters for a specific model objective. Challenger(s) refers to the subsequent models deployed for the same model objective but with different techniques used for predictions.When several prediction models (challengers) are deployed and one of the challengers produces optimum output meeting all the prediction requirements, the same can be set as Champion.This section includes the following topics:

Accessing Champion Challenger

Searching for Champion Challenger

Requesting New Champion

Accessing Champion ChallengerTo access the Champion Challenger page, follow these steps:






6. Click Model Management and then click Champion Challenger. The Champion Challenger window is displayed.

Figure 31. Champion Challenger



Requesting New Champion

To request a new champion, follow these steps:

1. Select the Model Objective, click and open the Model Objective browser.

2. Select the required Model Objective in the Hierarchy list and click .

3. Click OK. The window is refreshed to display the available models.

4. Select the checkbox adjacent to the model whose status has to be set to champion.

5. Click in the Champion Challenger toolbar. The Request New Champion window is displayed.

6. Select the Request New Champion checkbox. If you have been mapped to MDLCHAMP function role, you can select Authorize checkbox to authorize new champion.

7. Click Save to update the changes and return to Champion Challenger window. In the Champion Challenger window, the status of the selected model is updated as Champion and Authorized.

Searching for Champion ChallengerThe Champion Challenger page allows you to search for a specific champion challenger.To search for Champion Challenger, follow these steps:

1. Navigate to the Champion Challenger page.

2. Enter the Model Name.



Configure Execution VenueChapter 7–Managing Modeling


The machine details and compiler details required for model execution is configured on the Model Execution Configuration window.This section includes the following topics:

Prerequisites

Accessing Configure Execution Venue

Adding Model Execution Configuration

Viewing Model Execution Configuration

Modifying Model Execution Configuration

Deleting Model Execution Configuration

Prerequisites

Configure as instructed in the section Configurations for OFSAAI Remote Invocation of Scripted Models Using Standard R Distributions of the OFSAAI Administration and Configuration Guide on OHC.

Accessing Configure Execution Venue

To access the Configure Execution Venue menu, follow these steps:





5. Click Configure Execution Venue. The Model Execution Configuration window is displayed.

Figure 32. Model Execution Configuration

Adding Model Execution Configuration

You can create Model Execution Configuration from the Model Execution Configuration window.


https://docs.oracle.com/cd/E60058_01/homepage.htm


To create a Model Execution Configuration, follow these steps:

1. Select button from the tool bar. The Configure Execution Venue window is displayed. The Add button is disabled if you select any existing Host in the grid.

2. Enter the Execution Venue Details. The fields are described in the following table:

Table 7. Execution Venue Details

Field DescriptionFields marked in red asterisk (*) are mandatory.Name Enter name for the execution venue. This field is case-sensitive and does not allow

duplication. For example, logical name “R_DF” is not allowed if a name “R_DF” exists.Ensure that there are no special characters such as `, {,},”, ‘, ~, <,>, /, \, and multiple spaces.

Language Select the language from the drop-down list. The options are: MATLAB and R.Input Data Type Select the input data type. The options are ORE Frame, Data Frame and HDFS

File.Machine DetailsUser Name Enter the user name with which Rserve is started on the remote machine.Password Enter the password for the preceding user.Primary IP For Runtime Processes Enter the machine's primary IP for runtime processes (IP address/host name of

the remote machine where Rserve is running).Shared Path Enter the path of the Remote Machine or Server, which provides complete access

rights to all users. The output of the executions will be created in the directory configured here. Note: Remote Machine or Server refers to the execution engine such as Rserve. The configured path should end with a directory separator at the end.

Output required in tables Select Yes if you require the output in OFSAA tables.Outputs required in CSV format Select Yes if you require the output in CSV format.Delete output files Select Yes if you want to delete the output files. By default, this is No.Class Name Enter the Class Name of the execution implementation.Is a primary node? Select this option if the machine is a primary node.SSH Port Enter the SSH port number of OFSAA for file transfer between remote machine

and OFSAA.SFTP Port Enter the SFTP port number of OFSAA for file transfer between remote machine

and OFSAA.MATLAB Specific ConfigurationsWebservice URL Enter the web service URL for executing script on MATLAB server.Compiler Machine DetailsUser name Enter the user name for the compiler machine where the MATLAB compiler is

running.Password Enter the password for the compiler machine.Primary IP For Runtime Processes Enter the machine's primary IP for runtime processes.Compiler Path Enter the compiler path where R code will be compiled.SSH Port Enter the SSH port number of OFSAA to transfer files to the MATLAB compiler

machine.



3. Once you have updated all the necessary details, click Save to save the configure execution venue details.

Viewing Model Execution Configuration

You can view the Model Execution Configuration from the Model Execution Configuration window.To view the details for an existing Model Execution Configuration, follow these steps:

1. Select the checkbox adjacent to the Host.

2. Click button in the tool bar. The Model Execution Configuration window is displayed with details in view mode.

Modifying Model Execution Configuration

You can modify the Model Execution Configuration from the Model Execution Configuration window. To modify an existing Model Execution Configuration, follow these steps:

1. Select the checkbox adjacent to the Host that you want to update.

2. Click button in the tool bar. The Model Execution Configuration window is displayed in edit mode.

Note: The Edit button is disabled if you select multiple Hosts.

3. Edit the Model Execution Configuration details as required. For more information on the fields, see Adding Model Execution Configuration.

4. Click Save to upload changes.

Deleting Model Execution Configuration

You can remove the Model Execution Configuration(s) that are no longer required in the system by deleting from the Model Execution Configuration window.To delete a Model Execution Configuration, follow these steps:

SFTP Port Enter the SSH port number of OFSAA to transfer files to the MATLAB compiler machine.

Script ConfigurationNote: This pane has two options: 1. Predefined scripts in the application and 2. Custom scripts that have to be uploaded.Predefined scripts

Select the required scripts and click to move to selected scripts. Click

and to change the order of the selected scripts.

Upload scripts Click Browse button to upload custom scripts.

Select the required scripts and click to move to selected scripts. Click

and to change the order of the selected scripts.

Click to view the details of the selected scripts.

Field Description



1. Select the checkbox adjacent to a Host.

2. Click button from the tool bar. A confirmation dialog is displayed.

3. Click OK to confirm deletion.


CHAPTER 8 Managing Stress Testing

This chapter provides information about Modeling in the Oracle Financial Services Enterprise Stress Testing application and step-by-step instructions to use this section.

This chapter includes the following options:

About Stress Testing

Stress Definition

About Stress Testing

Stress testing is an integral part of a bank's risk measurement system and plays an important role in estimating the effects of potential financial crises on a bank's operations. Stress Testing or risk estimation technique, refers to the

process of examining the stability of a system or entity in adverse conditions. It involves testing beyond normal operational capacity, often to a breaking point, in order to observe the results. It also helps banks conduct analysis to estimate the impact of movements in the variables on specific measures such as profitability and capital adequacy.The Stress Testing utility supports the stress testing requirements across the entire suite of OFSAA products. It allows banks to define shocks and assess the impact of such shocks across multiple business areas.The two commonly accepted forms of Stress Testing are:

Sensitivity Analysis: Shocks are applied on a single variable.

Scenario Analysis: A scenario is defined as a shock to a single variable or a collection of shocks on multiple variables. Scenario analysis involves applying simultaneous shocks on multiple variables to assess the impact of scenario on a measure or a set of measures. Scenarios are further classified into the following categories:

Historical Scenarios: These scenarios replicate the past events docket. They are defined by specifying shocks to variables such that they replicate the movement seen during historical events. For example, the user may define a scenario that replicates the movement in stock market indices as observed during the catastrophic event. This scenario can then be applied to the current trading book portfolio of the bank to estimate the loss that might be incurred if a catastrophic event occurs. However, the historical scenarios may not cover the entire range of potential adverse conditions

Hypothetical Scenarios: These scenarios are based on user judgment and addresses the other possible adverse movements in the variables.


Stress DefinitionChapter 8–Managing Stress Testing

Figure 33. Stress Testing Workflow

Stress Definition

Stress refers to modeling a series of scenarios to analyze the impact of extreme market conditions and to measure the risk. This module enables you to create a stress definition. You can either specify a stand-alone execution of a Scenario, or map a Scenario(s) to a Baseline Run. The integrated process helps you to assess the impact of a Scenario across multiple areas. You can also view, modify, and delete stress definitions.This section includes the following topics:

Accessing Stress Definition

Adding Stress Definition

Executing Stress Definition

Viewing Stress Definition

Modifying Stress Definition

Deleting Stress Definition

Searching Stress Definition



Accessing Stress Definition

To access the Stress Definition page, follow these steps:





5. Click Stress Testing and then click Stress Definition. The Stress Definition window is displayed.

Figure 34. Stress Definition Page

Adding Stress Definition

Stress definition can be based on stress scenarios or business assumptions. A business assumption is essentially a process comprising of multiple rules which facilitate the computation of the impact of the assumption on base cash flows. Business assumptions are non-variable based. Scenario based stress testing involves applying simultaneous shocks on multiple variables to assess the impact of scenario on a measure or a set of measures.To add stress definition, follow these steps:



1. Select from the Stress Definition List tool bar. The Stress Definition Add window is displayed. The Add button is disabled if you have selected any checkbox in the grid.

Figure 35. Stress Definition Page

2. Enter the Stress Definition details as described in the following table:Table 8. Stress Definition - Field Descriptions

Field DescriptionFields marked in red asterisk (*) are mandatory.Stress Name Enter a name for the stress definition. Ensure that there are no special characters

like `, {,},", ', ~, <,>, /, \, and multiple spaces.Stress Definition Description Enter the required description for the stress definition.Segment Select the Segment from the drop-down list. The list displays all segments mapped

to your user group.Base Line Run

1. Click and open the Baseline Run browser. The available Baseline Runsin the selected Segment are listed in the Hierarchical pane.

Note: 2. Select the required Baseline Run based on which stress definition is to be

created and click .3. Click OK.

4. Click and view the selected baseline Run details.5.

6. In the Run Details grid, all the processes and tasks associated with the selected baseline run is displayed.

You can insert a new process to the selected Baseline run.

You can replace a process in the selected baseline run with another process. The process replacement can happen only for the processes which are directly placed under the run.

Select the checkbox adjacent to the process and click to replace it with another process. The Tasks Browser window displays all the processes which are defined in the selected segment and infodom.



Select the required process from the list and click . You cannot select a process which is already present in the baseline run.

You can modify or replace a task within a process with another task.

Select the checkbox adjacent to the task and click . The Tasks Browser window displays all the tasks which are defined in the selected segment and infodom.

Select the required process from the and click .

Select a task and click to delete it.

Select a process and click to delete it.

Set the precedence in which the tasks need to be executed.

Click adjacent to the Precedence field of a task. The available scenarios/ tasks are listed in the Precedence Browser window.

Select the tasks in the order in which they need to be executed and click . You can change the order

by clicking and .

Provide the Parameters for the task in the Parameters field.

Note: If the configuration is set to RRF, then the Parameters field value must be specified within double quotes.

7. Click Save. The stress definition details are saved and displayed in the Stress Definition window.

Executing Stress Definition

After creating stress definition, you need to create Manage Run Definition. Based on the Run Type (Single Request or Multiple Request), you need to execute the stress definition from Batch Execution window or Batch Group Execution window respectively. You can also monitor the status of stress definition execution.To execute stress definition, follow these steps:






6. Click Execution and then click Manage Run Execution. The Manage Run Execution window is displayed.

7. Click from the List toolbar. The Manage Run Definition window is displayed.



8. Click in the Run field and select the Stress Definition that you want to execute from the Run Selector window. For more information, see the Manage Run Definition section in the Oracle Financial Services Analytical Applications Infrastructure User Guide.

9. Click Save.

10. Execute the stress definition:

If you have selected Type as Single Request in the Manage Run Execution window:

a. Go to Execution > Batch Execution. The Batch Execution window is displayed. For more information on how to execute a Batch, see the Execute Batch section in the Oracle Financial Services Analytical Applications Infrastructure User Guide.

b. Click Execute Batch.

If you have selected Type as Multiple Request in the Manage Run Execution window:

a. Go to Execution > Batch Group Execution. The Batch Execution window is displayed. For more information on how to execute a Batch Group, see the Batch Group Execution section in the Oracle Financial Services Analytical Applications Infrastructure User Guide.

b. Click Start.To monitor the stress execution:

For Batch Execution:

a. Go to Execution > Batch Monitor. The Batch Monitor window is displayed.

b. Select the Stress Definition whose execution you want to monitor and select the Batch Run details. For more information, refer to the Monitor Batch section in the Oracle Financial Services Analytical Applications Infrastructure User Guide.

c. Click to start monitoring.

To view log files:

a. Login to Oracle Financial Services Enterprise Modeling.

b. Click from the header to display the applications in a Tiles menu.

c. Select Financial Services Enterprise Modeling from the Tiles menu. The Enterprise Modeling Application Home Page is displayed with the Navigation list to the left.

d. Click Common Tasks from the Navigation list to expand view the menu.

e. Click Operations to view the menu.

f. Click Processing Report. The Batch Processing Report window is displayed.

g. Select the Information Date and Batch Status from the drop-down lists. For more information, see the Batch Processing Reports section in the Oracle Financial Services Analytical Applications Infrastructure User Guide.

Viewing Stress Definition

You can view individual Stress Definition details at any given point.



To view the existing Stress Definition details, follow these steps:

1. Select the checkbox adjacent to the Stress ID.

2. Click in the Stress Definition tool bar. The Stress Definition View window is displayed with the stress definition details along with the mapped scenarios.

Modifying Stress Definition

You can modify only the Stress Description and Scenario for any selected Stress Definition. To modify an existing Stress Definition, follow these steps:

1. Select the checkbox adjacent to the Stress ID whose details are to be updated.

2. Click in the Stress Definition List tool bar. The Edit button is disabled if you have selected multiple Stress IDs. The Stress Definition Edit window is displayed.

3. Edit the Stress Definition details as required. For more information, refer to Add Stress Definition.

Note: While editing Stress Definition, you cannot unmap a scenario that is mapped to the stress definition. You can only change the version of the scenario.

4. Click Save to save changes.

Deleting Stress Definition

You can remove the stress definitions which are no longer required in the system, by deleting from the Stress Definition Summary window.

Note: Stress definitions, which use PR2 Base Line Run and has batch registered for processing, cannot be deleted. However, Stress definitions, which use RRF Base Line Run, can be deleted irrespective of whether it has batch registered for processing.

To delete a Stress Definition, follow these steps:

1. Select the checkbox adjacent to the Stress ID whose details are to be removed.

2. Click in the Stress Definition List tool bar.


Searching Stress Definition

The Stress Definition page allows you to search specific model execution request.To search for Stress Definition, follow these steps:

1. Navigate to the Stress Definition page.

2. Enter the Stress Definition Name.



CHAPTER 9 Creating and publishing models using integration of OFS EM with EMF Studio

The Oracle Financial Services Enterprise Modeling (OFS EM) application allows creating and publishing Models from external applications such as Enterprise Modeling Framework (EMF) Studio. The advantage is that Model Creation, which is done through the OFS EM application, is also possible through the integration of OFS EM with applications such as EMF Studio.

EMF Studio is browser-based workbench for creating Models and publishing to OFSAA. It provides a system for rapid creation and deployment of Models. RScript queries on Workspaces derives data from existing scenarios and you can test the scenarios with what-if analysis. What-if analysis helps you visualize data and lets you create Models

that are fit to be published. You can access the metadata from OFSAA and use it to create Models within Notebooks, which you can publish back on OFSAA. This chapter includes the following topics:

Prerequisites

Logging into EMF Studio

Creating Notebooks

Creating Paragraphs

Publishing Models

Using python to create notebooks and paragraphs, and publishing models

Prerequisites

The prerequisites to use this functionality on OFS EM are in the following list:

1. Users must have the requisite permissions to access EMF Studio.

2. Users must know how to use EMF Studio for features such as Creating Notebooks and Paragraphs.

3. EMF Studio servers must be up and running. See the OFS AAAI Applications Pack Installation Guide for more information.

4. OFS EM studio service must be up and running. See the OFS AAAI Applications Pack Installation Guide for more information.

5. Users must have Sandboxes, Datasets and Variables created in the OFS EM application to create Models. See previous chapters in this guide for more information.






Logging into EMF StudioChapter 9–Creating and publishing models using integration of OFS EM with EMF Studio

Logging into EMF Studio

Login to EMF Studio and create Notebooks. The following is the procedure to login to EMF Studio:

1. Enter the URL for EMF Studio in a web browser to display the Login window.

Figure 36. EMF Studio Login Window

2. Enter the details in the Username and Password fields, and click Login to display the EMF Studio Home window.

Note: The user name and password for login is your OFSAA login ID and password.


Creating NotebooksChapter 9–Creating and publishing models using integration of OFS EM with EMF Studio

Creating Notebooks

Notebooks contain a collection of Paragraphs, which is used to code or query the database. The EMF Studio Home window displays the Notebooks that are mapped to your role. It displays details for the Notebook’s Name, Author and Tags.

The following is the procedure to create a Notebook:

1. Click Create from the EMF Studio Home window to view the Create Notebook popup.

Figure 37. EMF Studio Home Window

2. On the popup, enter details for Name and Description. Enter tag details in Tag, if required.

Figure 38. Create Notebook Popup

3. Select from the relevant option for the type of Notebook from Type. For OFS EM, select from the options emfr and emfore for R Language, or select Default for Python.


Creating NotebooksChapter 9–Creating and publishing models using integration of OFS EM with EMF Studio

4. Click Create to create the Notebook and view the Paragraph window.

Editing a Notebook

The following is the procedure to edit Notebooks:

1. Click a Notebook from the list of Notebooks from the EMF Studio Home window to view the Paragraph window.

2. Click to view the Notebook Details popup. Modify the details if required.

Figure 39. Notebook Details Popup

3. Enter details for Name and Description. Enter tag details in Tag, if required or click X to remove existing tags.

4. Select the relevant option for the type of Notebook from Type.

5. Click Save.


Creating ParagraphsChapter 9–Creating and publishing models using integration of OFS EM with EMF Studio

Creating Paragraphs

Create Paragraphs to run your code or query using the various interpreter-friendly scripting languages supported in EMF Studio. You can use Paragraphs to query the database and extract datasets and variables information. You can then use this information to apply formulas and construct Models, which you can publish to OFSAA.

1. Click a Notebook from the list of Notebooks from the EMF Home window to view the Create Paragraph window. This window is displayed if you are creating a Paragraph for the first time in the Notebook. The following is an example:

Figure 40. Create Paragraph WindowNote: If you have created a Paragraph, the Paragraph window is displayed.

2. Click Create Paragraph to view the Paragraph window.

Figure 41. Paragraph Window

3. Click Workspaces to select the required Workspace. If there’s only one Workspace, then by default, the Workspace is displayed in the drop-down field.



4. Enter a Dataset query using RScript in the Paragraph form and click Run to view the result in the

form area following the query. Click HTML to view the result in HTML View format. The following illustration is an example:

Figure 42. Dataset query result example in EMF Studio

5. Hover the mouse pointer over the top or bottom of the form to display the set of buttons as shown in the

following illustration and click Add Paragraph button to add a new paragraph.

Figure 43. Hover mouse pointer to view Add Paragraph button

6. Enter a RScript query, in the new Paragraph form, for the Variables selected from the list shown for the Dataset.



7. Click Run to view the result in the form area following the query. Click HTML to view the result in HTML view format. This is similar to the query operation described previously for Datasets.

Figure 44. Variables query result example in EMF Studio

8. Hover the mouse pointer over the top or bottom of the form to display the set of buttons and click Add

Paragraph button to add a new paragraph.

9. Enter a RScript query, in the new paragraph form, for the Dataframes selected from the list shown for the

Variables. Click Run to view the result in the form area following the query. Click Table to view the result in Table view format.

Figure 45. Dataframes query result example in EMF Studio



11. Enter RScript formula queries that can give visual representation of modified data. You can use this to extend on the available dataframes data and create views of Models in the new Paragraph form.



12. Click Run to view the result in the form area following the query. Click HTML to view the result in HTML view format.

Figure 46. Example of RScript formula application on dataframes result in EMF StudioYou can query using various combinations of formulas and derive Models that fit your requirement. You can then publish these Models to OFSAA.


Publishing ModelsChapter 9–Creating and publishing models using integration of OFS EM with EMF Studio

Publishing Models

After creating Models from the Paragraph window, you can publish it to OFSAA. The following is the procedure to publish:

1. Click to view the Publish Notebook popup.

Figure 47. Publish Notebook Popup

2. Click Publish. The Notebook is published to OFSAA as a Model. Similarly, you can republish Notebooks

to overwrite with changes to the existing Notebooks. Click to view the Republish Notebook popup. Click Publish.

Figure 48. Republish Notebook Popup


Using python to create notebooks and paragraphs, and publishing modelsChapter 9–Creating and publishing models using integration of OFS EM with EMF Studio

Using python to create notebooks and paragraphs, and publishing models

The previous sections describe creating Notebooks and Paragraphs, and publishing Models for R Language. For most purposes, the procedure is similar for Python, except that there are a few minor differences, which are described in this section. The procedure for Python is in the following:

1. Create Notebooks. See Creating Notebooks for detailed information.

Note: For Python, select Default for the type of Notebook from Type.

2. Create Paragraphs using Python scripting. See Creating Paragraphs for detailed information.

Note: For Python, Workspaces and Publish is not available on the Paragraph window. This requires that you enter instructions in Python format in the Paragraph forms to create Paragraphs and Publish the Models to OFSAA.

3. Enter a query using Python scripting, in the Paragraph form, to derive a list of workspaces. Click Run

to view the result in the form area following the query. Click HTML to view the result in HTML View format. The following illustration is an example:

Figure 49. List of workspaces for python result exampleNote: Queries or scripts for Python, entered in the Paragraphs for Data Studio, must begin with %emfpy.



5. Enter a query using Python scripting, in the new Paragraph form, to select the workspace from the list derived in the previous steps.



6. Click Run to view the result in the form area following the query. Click HTML to view the result in HTML view format. This is similar to the query operation described previously.

Figure 50. Attach workspace python query result in EMF Studio



8. Enter the list of Dataset query using Python in the Paragraph form and click Run to view the result

in the form area following the query. Click HTML to view the result in HTML View format. The following illustration is an example:

Figure 51. Dataset python query result example in EMF Studio



10. Enter the Python query, in the new Paragraph form, for the Variables selected from the list shown for the Dataset.



11. Click Run to view the result in the form area following the query. Click HTML to view the result in HTML view format. This is similar to the query operation described previously for Datasets.

Figure 52. Variables python query result example in EMF Studio



13. Enter the Python query, in the new paragraph form, for the Dataframes selected from the list shown for

the Variables. Click Run to view the result in the form area following the query. Click Table to view the result in Table view format.

Figure 53. Dataframes python query result example in EMF Studio



15. Enter the Python formula queries that can give visual representation of modified data. You can use this to extend on the available dataframes data and create views of Models in the new Paragraph form.



16. Click Run to view the result in the form area following the query. Click HTML to view the result in HTML view format.

Figure 54. Example of python formula application on dataframes result in EMF Studio

17. You can query using various combinations of formulas and derive Models that fit your requirement. You can then publish these Models to OFSAA.

Note: The publishing procedure for Python varies from the publishing procedure for RScript. For RScript publish, see Publishing Models.

18. Enter the Python instruction to register and publish the model to OFSAA. Click Run to publish and

view the result in the form area following the query. Click HTML to view the result in HTML view format.

Figure 55. Example of python instruction to publish to OFSAAThe model is published to OFSAA.


APPENDIX A References

This section of the document consists of information related to intermediate actions that needs to be performed while completing a task. The procedures are common to all the sections and are referenced where ever required. You can refer to the following sections based on your need.


R Scripts and Oracle R Enterprise (ORE) Statistical FunctionsAppendix A

R Scripts and Oracle R Enterprise (ORE) Statistical Functions

Data usage when R script is used entirely

While defining a model scripted in R, user can select Dataset and Variables to assign data (table columns) to the R/ ORE objects used within the script. Oracle Financial Services Enterprise Modeling application prepares data from the dataset, variables and other attributes like filters chosen for the model and will make the same available as user specified R objects/ frames.No specific treatment is required in the script for using the data. User can just have an R data.frame object say 'GDP' and a variable (table.column) assigned to it and use 'GDP' as is in the script.

Data usage when ORE native implementation is employed - Oracle Financial Services Enterprise Modeling application - Specifics to be considered

The data which is prepared from the dataset and variables as explained in the previous section, will be available in this case as a named ORE ore.frame object ('OFSDATASET') when ORE native implementations are used (that is, when the Is ORE implementation used? checkbox is selected). Hence, the R objects(data) must always be accessed through the ore.frame object 'OFSDATASET'. For instance, if two objects say 'x' and 'y' are used and dataset/ variables are chosen for 'x' and 'y' in the model definition, then the objects 'x' and 'y' should be accessed in the script as 'OFSDATASET$x' and 'OFSDATASET$y' respectively.An illustration to explain the R and ORE cases is given in the following section:-For a simple regression model which is entirely scripted in R, that uses a dataset and three variables (DependentVariable, IndependentVariable1 and IndependentVariable2), the user should not select the Is ORE implementation used? checkbox. In this case the R script is as follows:art.mod<-lm(DependentVariable ~ IndependentVariable1 + IndependentVariable2)art.summ<-summary(art.mod)coef( art.summ )art.summ[[ "r.squared" ]]

#do some line plotsnew.x.datafrme = data.frame(x=seq(from=range(IndependentVariable1)[ 1 ],to=range( IndependentVariable1)[2],length=length(DependentVariable)))

Here the variables chosen for the model are accessed directly in the script as IndependentVariable1, IndependentVariable2, and DependentVariable.

Whereas for a parallel ORE implementation of the same (a dataset and three variables DependentVariable, IndependentVariable1 and IndependentVariable2) using ORE statistical functionalities, user must check the Is ORE implementation used? checkbox and the script is as follows:

art.mod<-ore.lm(DependentVariable ~ IndependentVariable1 + IndependentVariable2, data=OFSDATASET)art.summ<-summary(art.mod)coef( art.summ )art.summ[[ "r.squared" ]]



#do some line plotsnew.x.orefrme = data.frame(x=seq(from=range(OFSDATASET$IndependentVariable1 )[ 1 ],to=range(OFSDATASET$IndependentVariable1)[2],length= length(OFSDATASET$DependentVariable)))#perform some operations on the new data

Here the variables chosen for the model are accessed from OFSDATASET as OFSDATASET$IndependentVariable1, OFSDATASET$IndependentVariable2, OFSDATASET$DependentVariable.

Data Handling

It is highly recommended that data required from the database should be pulled through the framework provided mechanism, that is, using dataset and variables and not with any explicit DB connections. This ensures proper security, authenticity, and auditing.Auditing is enabled in the definition windows by introducing audit trials that captures and displays the user details and the date of creation/ modification, along with comments.For instance, here is a sample script where data is fetched from the DB directly (not through framework). This way of accessing the database resident data in a model is not recommended.con<-dbConnect(Oracle(),"userName","password")qry<-"select EventLoss as Y, CardType as X1, AccBalance as X2, CustSalary as X3 from CustTable where Default= 'Y' "res<- dbSendQuery(con, qry)OperationalData<-fetch(res) dbDisconnect(con)

#Model LogicNewRegModel<-lm(Y~X1+X2+X3, data= OperationalData)Plot(NewRegModel)NewRegModel

Here is how a script for the same purpose can be created for accessing data via framework dataset and variables. Define 'EventLoss', 'CardType', 'AccBalance', and 'CustSalary' from the table 'CustTable' as variables in the Oracle Financial Services Enterprise Modeling application. while defining the model, select these variables and assign them to R objects (the R names used within the script) say, 'IndepVariable1', 'IndepVariable2', 'IndepVariable3' and 'DepVar' from the Configure Inputs window.

#Assuming EventLoss, CardType, AccBalance, CustSalary from #CustTable are defined as variables in a data set, and that #dataset is selected for variable assignments to the respective R #variable names: Y, X1, X2, and X3.

NewRegModel<-lm(DepVar ~ IndepVariable1+ IndepVariable2+ IndepVariable3)

#The variables are made directly available to the R #environment by the framework

Plot(NewRegModel)NewRegModel



Logging

R processing log that captures script processing information, any warnings, errors or exceptions from the script, gets generated in the database server at '$ORACLE_HOME/dbs', since the R executable runs completely on the database server. Purging of the files is recommended at a regular basis.


Fitting and Prediction Using R ScriptAppendix A

Fitting and Prediction Using R Script

In order to support fitting and prediction using R script, the output objects can be stored in datastore using ore.save() during fitting and retrieve it using ore.load() during prediction.ore.save() saves an R object or a list of R objects to the specified datastore in the current user's schema of the connected database.ore.load() loads all of the R objects stored in a specified datastore from the current user's schema of the connected database.For more information, see the Oracle® R Enterprise User's Guide (E36761-08).The approach to use ore.save() and ore.load() is explained with the following example:

R scripting

Fitting:

fit<-lm(y~x)ore.save(fit, name='regression_fit', overwrite=TRUE)

ore.save() will store the R object 'fit' in the datastore with the name 'regression_fit'. overwrite is a logical value specifying whether to overwrite the datastore if it already exists; the default is FALSE. The overwrite flag is set to true to make sure that ore.save() does not fail. This is one way of doing it; otherwise the user's script should check for the existence of the datastore object.

Prediction:

ore.load('regression_fit')predict<-predict(fit)

ore.load() is going to load all the R objects in the datastore 'regression_fit'.From the datastore user should know the R object name to be used in the predict call.

ORE scripting

The approach is same as explained above, but the Is ORE implementation used? checkbox to be checked.

Fitting:

ore_fit<-ore.lm(y~x, data=OFSDATASET)ore.save(ore_fit, name='ore_regression_fit', overwrite=TRUE)

Prediction:

ore.load('ore_regression_fit')predict<-ore.predict(ore_fit, newdata=OFSDATASET)


Fitting and Prediction Using R ScriptAppendix A


APPENDIX B User Groups

User Groups

Table 9. User Groups

User Group Name User Group DescriptionModeling Administrator User mapped to this group will have access to all the menu items for Enterprise

modeling and will be have authorization rights for sandbox population, model deployment and modeling technique authorization.

Modeler Group User mapped to this group will have access to all the menu items for Enterprise modeling but will not have authorization rights for sandbox population, model deployment and modeling technique authorization.


User Group - Role MappingAppendix B

User Group - Role Mapping

Table 10. User Group to Role Mapping

Group Name Role NameModeler Alias AccessModeler Alias Read OnlyModeler Alias WriteModeler Atomic excel upload writeModeler BMM Hierarchy AccessModeler BMM Hierarchy Read OnlyModeler BMM Hierarchy WriteModeler BMM Processor AccessModeler BMM Processor Read OnlyModeler BMM Processor WriteModeler Batch AccessModeler Batch Read OnlyModeler Batch WriteModeler Config excel advancedModeler DEFQ ManagerModeler DEFQ writeModeler DI WriteModeler DMM WriteModeler DQ AccessModeler DQ AdvancedModeler DQ ReadModeler DQ WriteModeler DT WriteModeler Dataset AccessModeler Dataset Read OnlyModeler Dataset WriteModeler Derived Entity AccessModeler Derived Entity Read OnlyModeler Derived Entity WriteModeler Dimension AccessModeler Dimension Read OnlyModeler Dimension WriteModeler ETL AnalystModeler Essbase Cube AccessModeler Essbase Cube Read OnlyModeler Essbase Cube WriteModeler MDB WriteModeler MF Access



Modeler MF AdvancedModeler MF ReadModeler MF WriteModeler Manage Run AccessModeler Manage Run Read OnlyModeler Manage Run WriteModeler Measure AccessModeler Measure Read OnlyModeler Measure WriteModeler Obj Migration AccessModeler Obj Migration ReadModeler ObjectAdmin advancedModeler Oracle Cube AccessModeler Oracle Cube Read OnlyModeler Oracle Cube WriteModeler Process AccessModeler Process Read OnlyModeler Process WriteModeler Rule AccessModeler Rule Read OnlyModeler Rule WriteModeler Run AccessModeler Run Read OnlyModeler Run WriteModeler STF AccessModeler STF AdvancedModeler STF ReadModeler STF WriteModeling Administrator Alias AccessModeling Administrator Alias AuthorizeModeling Administrator Alias Read OnlyModeling Administrator Alias WriteModeling Administrator Atomic excel advancedModeling Administrator Atomic excel upload writeModeling Administrator BMM Hierarchy AccessModeling Administrator BMM Hierarchy AuthorizeModeling Administrator BMM Hierarchy Read OnlyModeling Administrator BMM Hierarchy WriteModeling Administrator BMM Processor AccessModeling Administrator BMM Processor Authorize


Group Name Role Name



Modeling Administrator BMM Processor Read OnlyModeling Administrator BMM Processor WriteModeling Administrator Batch AccessModeling Administrator Batch AdvancedModeling Administrator Batch AuthorizeModeling Administrator Batch PhantomModeling Administrator Batch Read OnlyModeling Administrator Batch WriteModeling Administrator Config excel advancedModeling Administrator DEFQ ManagerModeling Administrator DEFQ accessModeling Administrator DEFQ advancedModeling Administrator DEFQ authorizeModeling Administrator DI WriteModeling Administrator DMM WriteModeling Administrator DQ AccessModeling Administrator DQ AdvancedModeling Administrator DQ AuthorizeModeling Administrator DQ ReadModeling Administrator DQ WriteModeling Administrator DT WriteModeling Administrator Dataset AccessModeling Administrator Dataset AuthorizeModeling Administrator Dataset Read OnlyModeling Administrator Dataset WriteModeling Administrator Derived Entity AccessModeling Administrator Derived Entity AuthorizeModeling Administrator Derived Entity Read OnlyModeling Administrator Derived Entity WriteModeling Administrator Dimension AccessModeling Administrator Dimension AuthorizeModeling Administrator Dimension Read OnlyModeling Administrator Dimension WriteModeling Administrator ETL AnalystModeling Administrator Essbase Cube AccessModeling Administrator Essbase Cube AuthorizeModeling Administrator Essbase Cube Read OnlyModeling Administrator Essbase Cube WriteModeling Administrator MDB WriteModeling Administrator MF Access





Modeling Administrator MF AdvancedModeling Administrator MF AuthorizeModeling Administrator MF ReadModeling Administrator MF WriteModeling Administrator Manage Run AccessModeling Administrator Manage Run Read OnlyModeling Administrator Manage Run WriteModeling Administrator Measure AccessModeling Administrator Measure AuthorizeModeling Administrator Measure Read OnlyModeling Administrator Measure WriteModeling Administrator Obj Migration AccessModeling Administrator Obj Migration AdvancedModeling Administrator Obj Migration ReadModeling Administrator Obj Migration WriteModeling Administrator ObjectAdmin advancedModeling Administrator Oracle Cube AccessModeling Administrator Oracle Cube AuthorizeModeling Administrator Oracle Cube Read OnlyModeling Administrator Oracle Cube WriteModeling Administrator PR2 AdministratorModeling Administrator Process AccessModeling Administrator Process AdvancedModeling Administrator Process AuthorizeModeling Administrator Process Read OnlyModeling Administrator Process WriteModeling Administrator Publish MetadataModeling Administrator Rule AccessModeling Administrator Rule AdvancedModeling Administrator Rule AuthorizeModeling Administrator Rule Read OnlyModeling Administrator Rule WriteModeling Administrator Run AccessModeling Administrator Run AdvancedModeling Administrator Run AuthorizeModeling Administrator Run Read OnlyModeling Administrator Run WriteModeling Administrator STF AccessModeling Administrator STF Advanced





Modeling Administrator STF ReadModeling Administrator STF Write




APPENDIX C EMF Object Migration

This section presents information related to EMF Object Migration prerequisites. For detailed information on how to migrate objects, see the Command Line Utility section in the Oracle Financial Services Analytical Infrastructure Applications User Guide.

Prerequisites

For Modeling Framework and Stress Testing objects:

Migrate the Sandbox before you migrate your Models. This is important since you can migrate Models to the same Sandbox across environments.

The availability of the application with same APP_ID in source and target environment is a prerequisite for Sandbox Migration.

If the “Data model upload required” checkbox is selected for the sandbox definition, the infodom code provided in SANDBOXINFODOM attribute should be a clean infodom where no model upload is done.

Do not perform Technique migration between infodoms, it is not allowed. However, you can perform Technique migration between environments.

Create Data Source for Sandbox Infodom to migrate R or ORE based models.

Note: Dependency migration of models is not supported. Since the dependent metadata for a model can be in the production infodom, a user who is granted rights to the sandbox may not have rights to the production infodom to create these dependent metadata. This would create a conflict and hence dependency migration of models cannot be done.






Appendix C


Oracle Financial Services Enterprise Modeling application 8.0.9.0.0 User Guide

Oracle CorporationWorld Headquarters500 Oracle ParkwayRedwood Shores, CA 94065U.S.A.

Worldwide Inquiries:Phone: +1.650.506.7000Fax: +1.650.506.7200www.oracle.com/us/industries/financial-services/

Copyright © 2019 Oracle Financial Services Software Limited. All rights reserved.

No part of this work may be reproduced, stored in a retrieval system, adopted or transmitted in any form or by any means, electronic, mechanical, photographic, graphic, optic recording or otherwise, translated in any language or computer language, without the prior written permission of Oracle Financial Services Software Limited.

Due care has been taken to make this Oracle Financial Services Enterprise Modeling 8.0.9.0.0 User Guide and accompanying software package as accurate as possible. However, Oracle Financial Services Software Limited makes no representation or warranties with respect to the contents hereof and shall not be responsible for any loss or damage caused to the user by the direct or indirect use of this Oracle Financial Services Enterprise Modeling application 8.0.9.0.0 User Guide and the accompanying Software System. Furthermore, Oracle Financial Services Software Limited reserves the right to alter, modify or otherwise change in any manner the content hereof, without obligation of Oracle Financial Services Software Limited to notify any person of such revision or changes.

All company and product names are trademarks of the respective companies with which they are associated.